J Mark Cock

Akira F Peters

Susana M Coelho

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Brown algae

J. Mark Cock 1,2 , Akira F. Peters 2,3 and Susana M. Coelho 1,2 What are brown algae? Brown algae or brown seaweeds are the dominant organisms in many of the world's coastal regions, where they are responsible for the bulk of primary production, often forming extensive undersea forests that host a high level of biodiversity. From an evolutionary point of view, brown algae are members of the stramenopiles (or heterokonts), a group that has been evolving independently of the well-studied animal, fungal and green plant lineages for over a billion years. As a result of this unique evolutionary history, these organisms exhibit many novel features. For example, they represent one of a very limited number of eukaryotic groups that have independently evolved complex multicellularity. Brown algae also exhibit some remarkable characteristics in terms of their cell biology; cytokinesis for example having features typical of both animals (centrosomes) and green plants (centrifugal cell plate formation). Moreover, since their divergence from other eukaryotic groups, brown algae have evolved several innovative metabolic processes, making them highly interesting targets for the discovery of novel biomolecules for industrial use. There is also increasing interest in the use of brown algae as a source of biomass for fuel production due to their very rapid growth rates and the fact that cultivation does not compete with food crops for the use of arable land. The giant kelp, Macrocystis pyrifera, for example, is one of the largest and most rapidly growing organisms on the planet, reaching lengths of up to 60 metres.

Has a model organism been developed

to study all these interesting aspects of brown algal biology? Yes, the small, filamentous brown alga Ectocarpus was proposed as a model organism for the brown algae about seven years ago and a number of genetic and molecular tools have been developed for this organism. Available tools include a considerable amount of cDNA and small RNA sequence data, various types of microarray, genetic methodologies and a genetic map. A transformation protocol, gene silencing techniques and a population adapted to the reverse genetic TILLING (Targeting Induced Local Lesions in Genomes) technique are under development.

Why was Ectocarpus chosen as the model for this group of organisms?

The choice of Ectocarpus was based primarily on the fact that, in contrast to the much larger kelps, this small, filamentous alga is well adapted for genetic and genomic approaches. The life cycle of Ectocarpus is relatively short (3-4 months) and can be completed in the laboratory using simple culture conditions (Figure 1). Genetic techniques, such as crosses and mutant screens are possible. Strains can be grown axenically and can be maintained for long-term storage under low light condition, and many ecotypes are available. Importantly though, despite their relatively simple morphology, the Ectocarpales are one of the more recently evolved brown algal groups and are closely related to complex kelps of the group Laminariales. Information obtained using Ectocarpus as a model organism should therefore be relevant to the economically and ecologically important kelps. Do we have a genome sequence for Ectocarpus? Yes, the complete genome sequence was published last year. As might have been expected, given the atypical evolutionary origin of this organism, the genome exhibits a number of remarkable features. For example, the genes are split into many short exons separated by long introns, and intergenic regions are often very short. One consequence of this is that intron sequence constitutes an unusually large proportion (40%!) of the genome.

What biological processes can

Ectocarpus be used to study? Work on Ectocarpus dates back to the 19th century. Investigators initially concentrated on understanding its life cycle, which involves alternation between two independent, multicellular generations, the sporophyte and the gametophyte. Later work led to the characterisation of the pheromone that is produced by the female gamete to attract male gametes and to the characterisation of a giant DNA virus, EsV-1, which integrates into the alga's nuclear genome following infection.

Current research programs investigate several aspects of Ectocarpus biology. The life cycle continues to be an important area of study, with modern genetic methods now being employed to investigate the molecular mechanisms that underlie key life cycle transitions. For example, mutants have been obtained in which the alternation between the sporophyte and gametophyte generations is perturbed. Other active areas of study include sex determination, morphogenesis, gamete recognition, various aspects of metabolism including carbon storage and cell wall biosynthesis, and responses to both biotic and abiotic stress.

You mentioned complex multicellularity, what is known about how this evolved in the brown algae?

Only five of the many phylogenetic groups of eukaryotic organisms are considered to have evolved complex multicellularity, which can be defined as the deployment of a developmental program that involves the creation of distinct organs made up of multiple different cell types. These five groups are the animals, green plants/algae, the fungi, the red algae and the brown algae. Multicellularity emerged independently in each of these groups, so that each can be considered to represent an individual evolutionary "experiment" and comparative analysis of the five lineages is expected to provide insights into the fundamental molecular processes underlying this key evolutionary transition. Unfortunately, very little is known about how developmental processes work in several of these groups, including the brown algae. The Ectocarpus genome sequence therefore presented an unparalleled opportunity to obtain some clues about how multicellularity might have emerged in this lineage. Analysis of the genome sequences identified several potentially important features, including the retention of key genes and gene families and the emergence of novel gene families. One particularly striking discovery was the identification of a family of membranelocalised receptor kinases, which is absent from unicellular members of the stramenopiles, such as the diatoms. Both animals and green plants possess families of membrane-localised receptor kinases. Importantly, not only do these molecules appear to have functions that are related to multicellular development, but they are encoded by gene families that evolved independently in the two lineages and their emergence may have been a key step along the path to multicellularity. Phylogenetic analysis of the Ectocarpus receptor kinase family indicated that it too evolved independently, substantially reinforcing the link between the invention of receptor kinases and the transition to multicellularity.

Where can I find out more?

Charrier, B., Coelho, S.M., Le Bail, A., Tonon, T., Michel, G., Potin, P., Kloareg, B., Boyen, C., Peters, A.F. and Cock. J.M. (2008)

Figure 1 .

 1 Figure 1. The Ectocarpus life cycle under laboratory conditions. (A) Ectocarpus sporophyte showing the upright filaments (arrow) that bear the reproductive structures emerging from a densely branched, prostrate base. Size bar = 200 µm. (B) DAPIstained (green) meiospores within a unilocular sporangium borne by a sporophyte. Size bar = 20 µm. (C) Germinating meiospores released from a single, isolated unilocular sporangium. The meiospores, which are all derived from a single meiotic event (followed by several mitoses), will develop to produce the gametophyte generation. Size bar = 100 µm. (D) Mature gametophyte filaments releasing gametes (dark spots) into the medium from plurilocular gametangia (arrow). Size bar = 100 µm.

  Development and physiology of the brown alga Ectocarpus siliculosus: two centuries of research. NewPhytol. 177, 319-332. Cock JM, Coelho SM, Brownlee C, Taylor AR. (2010). The Ectocarpus genome sequence: insights into brown algal biology and the evolutionary diversity of the eukaryotes. New Phytol., 188: 1-4, and other articles in this special issue on the Ectocarpus genome. Cock, J.M., Sterck, L., Rouzé, P., Scornet, D., Allen, A.E., Amoutzias, G., Anthouard, V., Artiguenave, F.

, Aury, J.-M.,

Badger, J.H., et al.. (2010)

. The Ectocarpus genome and the independent evolution of multicellularity in the brown algae. Nature 465, 617-621.


Octavia Efraim
email: octavia-edie.efraim@univ-rennes2.fr

Vladislav Maraev
email: vladislav.maraev@gu.se

João Rodrigues
email: joao.rodrigues@di.fc.ul.pt

Boosting a rule-based chatbot using statistics and user satisfaction ratings

Using data from user-chatbot conversations where users have rated the answers as good or bad, we propose a more efficient alternative to a chatbot's keyword-based answer retrieval heuristic. We test two neural network approaches to the near-duplicate question detection task as a first step towards a better answer retrieval method. A convolutional neural network architecture gives promising results on this difficult task.

Introduction

A task-oriented conversational agent which returns predefined answers from a fixed set (as opposed to generating responses in real time) can provide a considerable edge over a fully-human answering system, if it handles correctly most of the repetitive queries which require no personalised answer. Indeed, at least in our experience, many of the questions asked by users and their expected answer look like entries in a list of frequently asked questions (FAQ): "What are your opening hours?", "Do you deliver to this area?", etc. An effective conversational agent, or chatbot, can act as a filter, sifting out such questions and only passing on to human agents those it is unable to deal with: those which are too complex (e.g. made up of multiple queries), those for which there simply is no response available, or those which require consulting a client database in order to provide a personalised answer (e.g. the status of a specific order or request). Such questions may occur at the very beginning or at some later point during a conversation between a customer and the automated agent. In the latter case, a well-performing chatbot will at least have saved human effort up to the moment where the difficulty emerged (provided it also hands on to the human a summary of the dialogue).

If the job of such retrieval-based conversational agents may seem easy enough to be successfully handled through a rule-based approach, in reality, questions coming from users exhibit much more variation (be it lexical, spelling-related, or syntactic) that is feasibly built into hand-crafted rules for question parsing. Approaches based on statistical learning from data may therefore benefit such answer retrieval systems.

Our goal is to improve on an existing closed-domain chatbot which returns answers from a closed set using keywords as a retrieval heuristic and human-defined priority rules to break ties between multiple candidate answers. Assuming a question does have an answer in the closed answer repository, this chatbot may fail to find it because it misunderstands the question (in which case it replies with the wrong answer) or because it is unable to "understand" it (i.e. map it to an available response) altogether (it then asks the user to provide an alternative formulation). This design means that the chatbot's ability to recognise that two distinct questions can be accurately answered by the same reply is very limited. Potential improvements to this system design may target the answer retrieval method, the candidate answer ranking method, and the detection of out-of-domain questions. We choose to address answer retrieval.

This paper is organised as follows: in Section 2 we review some tasks and solutions which are potentially relevant to our goal; Section 3 gives an overview of the system we set out to improve; Section 4 describes the data available to us, and our problem formulation; in Section 5 we outline the procedure we applied to our data in order to derive from it a new dataset suited to our chosen task; Section 6 gives an account of our proposed systems; in Section 7 we sum up and discuss our results; finally, Section 8 outlines some directions for follow-up work.

Related work

The ability to predict a candidate answer's fitness to a question is a potentially useful feature in a dialogue system's answer selection module. A low-confidence score for a candidate answer amounts to a problematic turn in a conversation, one that warrants corrective action. Addressing success/failure prediction in dialogue, both [START_REF] Walker | Learning to predict problematic situations in a spoken dialogue system: experiments with how may I help you?[END_REF] (human-computer dialogues in the customer relationship domain) and [START_REF] Reitter | Predicting success in dialogue[END_REF] (humanhuman task-oriented dialogues) distinguish between a predictive task with immediate utility for corrective action in real time, and a post-hoc estimation task for analysis purposes. If the former authors learn a set of classification rules from meta-textual and meta-conversational features only, the latter find that, with an SVM classifier, lexical and syntactic repetition reliably predict the success of a task solved via dialogue.

Answer selection for question answering has recently been addressed using deep learning techniques. In [START_REF] Feng | Applying deep learning to answer selection: A study and an open task[END_REF], for instance, the task is treated as a binary classification problem over question-answer (QA) pairs: the matching is appropriate or not. The authors propose a language-independent framework based on convolutional neural networks (CNN). The power of 1-dimensional (1D) convolutional-and-pooling architectures in handling language data stems from their sensitivity to local ordering information, which turns them into powerful detectors of informative n-grams [START_REF] Goldberg | Neural network methods for natural language processing[END_REF]. Some of the CNN architectures and similarity metrics tested in [START_REF] Feng | Applying deep learning to answer selection: A study and an open task[END_REF] on a dataset from the insurance domain achieve good accuracy in selecting one answer from a closed pool of candidates.

The answer selection problem has also been formulated in terms of information retrieval. For example, [START_REF] Jijkoun | Retrieving answers from frequently asked questions pages on the web[END_REF] reports on an attempt to answer open-domain questions asked by users on Web forums, by searching the answer in a large but limited set of FAQ QA pairs collected in a previous step. The authors use simple vector-space retrieval models over the user's question treated as a query and the FAQ question, answer, and source document indexed as fields making up the item to be returned. Also taking advantage of the multi-field structure of answers in QA archives, [START_REF] Xue | Retrieval models for question and answer archives[END_REF] combines a translation-based language model estimated on QA pairs viewed as a parallel corpus, and a query likelihood model with the question field, the answer field, and both combined. A special application of information retrieval, SMS-based FAQ retrieval -which was proposed as a shared task at the Forum for Information Retrieval Evaluation in 2011 and 2012 -faces the additional challenge of very short and noisy questions. The authors of [START_REF] Hogan | Dcu@ fire 2011: Sms-based faq retrieval[END_REF] break the task down into: question normalisation using rules learnt on several corpora annotated with error corrections; retrieval of a ranked list of answers using a combination of a term overlap metric and two search engines with BM25 as the ranking function, over three indexes (FAQ question, FAQ answer, and both combined); finally, filtering out-of-domain questions using methods specific to each retrieval solution.

Equating new questions to past ones that have already been successfully answered has been proposed as another way of tackling question answering. Such duplicate question detection (DQD) approaches fall under near-duplicate detection, and are related to paraphrase identification and other such instances of the broader problem of textual semantic similarity, with particular applications, among others, to community question answering (cf. Task 3 at SemEval-2015, 2016, and 2017). In turn, DQD may be cast as an information retrieval problem [START_REF] Bernhard | Answering learners' questions by retrieving question paraphrases from social Q&A sites[END_REF], where the comparison for matching is performed on different entities: new question with or without its detailed explanation if available, old question with or without the answer associated with it; where the task is not to reply to new questions, but rather to organise a QA set, answers have even been compared to each other in order to infer the similarity of their respective questions [START_REF] Jeon | Finding semantically similar questions based on their answers[END_REF]. Identifying semantically similar questions entails at least two major difficulties: similarity measures targeted at longer documents are not suited to short texts such as regular questions; and word overlap measures (such as Dice's coefficient or the Jaccard similarity coefficient) cannot account for questions which mean the same but use different words. Notwithstanding, word overlap features have been shown to be efficient in certain settings [START_REF] Muthmann | An automatic approach for identifying topical near-duplicate relations between questions from social media q/a sites[END_REF][START_REF] Jalbert | Automated duplicate detection for bug tracking systems[END_REF]. CNN architectures, which, since their adoption from computer vision, have proved to be very successful feature extractors in text processing [START_REF] Goldberg | Neural network methods for natural language processing[END_REF], have recently started to be applied to the task of DQD. [START_REF] Bogdanova | Detecting semantically equivalent questions in online user forums[END_REF] reports impressive results with word-based CNN on data from the StackExchange QA forum. In [START_REF] Rodrigues | Ways of asking and replying in duplicate question detection[END_REF], the authors obtain very good performance on a subset of the AskUbuntu section of StackExchange by combining a similar word-based CNN with an architecture based on [START_REF] Afzal | Mayonlp at semeval-2016 task 1: Semantic textual similarity based on lexical semantic net and deep learning semantic model[END_REF].

Answer relevancy judgements by human annotators on the output of dialogue systems are a common way of evaluating this technology. The definition of relevancy is tailored to each experimental setup and research goal. In [START_REF] Ritter | Data-driven response generation in social media[END_REF] annotators assess whether the answer generated by a system based on statistical machine translation in reply to a Twitter status post is on the same topic as that post and "makes sense" in response to it. More recently-to cite just one example taken from a large body of work on neural response generation-, to evaluate the performance of the neural conversation model in [START_REF] Vinyals | A neural conversational model[END_REF], human judges are asked to choose the better of two replies to a given question: the output of the experimental system and a chatbot. The role of human judgements in such settings is nonetheless purely evaluative: the judge assesses post hoc the quality of a small sample of the system output according to some relevancy criterion. In contrast to these experiments, ours is not an unsupervised response generation system, but a supervised retrieval-based system, as defined in [START_REF] Lowe | On the evaluation of dialogue systems with next utterance classification[END_REF], insofar as it does "explicitly incorporate some supervised signal such as task completion or user satisfaction". Our goal is to take advantage of this feature not only for evaluation, but also for the system's actual design. As far as the evaluation of unsupervised response generation systems goes, this is a challenging area of research in its own right [START_REF] Lowe | On the evaluation of dialogue systems with next utterance classification[END_REF][START_REF] Liu | How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation[END_REF].

Overview of the rule-based chatbot

The chatbot we are aiming at improving is deployed on the website of a French air carrier as a chat interface with an animated avatar. The system was developed by a private company and we had no participation in its conception or implementation. Its purpose is, given a question, to return a suitable predefined answer from a closed set. The French-speaking chatbot has access to a database of 310 responses, each of which is associated unambiguously with one or more keywords and/or skip-keyphrases (phrases which allow for intervening words). An answer is triggered whenever the agent detects in the user's query one of the keywords or keyphrases associated with that answer. A set of generic priority rules is used to break ties between competing candidate answers (which are simultaneously induced by the concurrent presence in the question of their respective keywords).

While this chatbot is closed-domain (air travel), a few responses have been included to handle general conversation (weather, personal questions related to the chatbot, etc.), usually prompting the user to go back on topic. A few other answers are given in default of keywords in the query: the chatbot informs the user that it has not understood the question, and prompts them to rephrase it. Some answers include one or several links either to pages on the company's website or to another answer; in the latter case, a click on the link will trigger a pseudo-question (a query is generated automatically upon the click, and recorded as a new question from the user). By virtue of its design, this system is deterministic: it will always provide the same answer given the same question.

The user interface provides a simple evaluation feature: two buttons (a smiling face and a sad face) enabling users to mark an answer as relevant or irrelevant to the query that prompted it. This evaluation feature is optional and not systematically used by customers. Exchanges with the chatbot usually consist of a single QA pair. There are, however, longer conversations too. Such dialogues can span a few minutes up to many hours, as no limit is imposed on the duration of a period of inactivity (the dialogue box does not close automatically). We improperly denote all input coming from a user as a question: in fact, in longer conversations a message can be phatic, evaluative of the previous answer of the chatbot's performance, it may convey information, or it may be asking a question properly.

Data

Our original data consists of QA pairs from conversations with the chatbot, where users have rated the system's answer using the smiley button. For our purposes, a smiling face rating amounts to a label of "good" and a sad face rating to a label of "bad", assigned to the answer in relation to the question. This binary assessment scheme is far from the complexity of the many multidimensional evaluation frameworks that have been proposed over time to assess the subjective satisfaction or acceptance of users of dialogue systems, chiefly spoken ones [START_REF] Walker | Paradise: A framework for evaluating spoken dialogue agents[END_REF][START_REF] Hone | Subjective assessment of speech-system interface usability[END_REF]. But, while a more nuanced evaluation scale might have been desirable, this simple binary scheme (which is not of our making, but was built into the system) is also lighter on the user. We do not equate the binary judgements with an objective measure of task success, because of their subjective component: many aspects of the user's experience with the system may influence the rating. Therefore we term the "good/bad" ratings in our data "user satisfaction ratings".

We have limited ourselves to one-turn dialogues (which are also the most common), in order to deal with self-contained questions. Our dataset contains 48,114 QA pairs from one-line dialogues. The proportions of classes are 0.28 for "good" and 0.72 for "bad". We conjecture that the predominance of negative ratings is partly a matter of negativity bias [START_REF] Baumeister | Bad is stronger than good[END_REF]: since customers are free, but not required, to evaluate the chatbot's answer, they may choose to do so mostly when they have strong feelings (which are more often negative) about it. Questions are relatively short (13 words and 70 characters on average; median values: 11 words and 57 characters), but there are a few outliers (a maximum of 241 words and 1357 characters).

Approach chosen

As mentioned above (Section 1), our goal is to improve the chatbot's performance on retrieving answers. We break down the answer retrieval problem into two steps:

1. Duplicate question detection (DQD). Given a question, classify it as semantically similar or dissimilar to questions from a set of past questions with known answers. 2. Answer selection. Select an answer to the new question based on the DQD output.

In this paper we address the DQD task. We define semantic similarity for the task at hand in line with the definition of semantic equivalence in [START_REF] Bogdanova | Detecting semantically equivalent questions in online user forums[END_REF], with an additional requirement as per [START_REF] Muthmann | An automatic approach for identifying topical near-duplicate relations between questions from social media q/a sites[END_REF]: we take two questions to be semantically similar if they can be correctly answered by the same answer, whose hypothetical existence suffices, provided that this answer is as specific as possible. As a point of terminology, "similarity" seems more permissive than "equivalence" as to how far two questions are allowed to diverge from one another: "What time does the flight to New York depart on Monday 12th?" and "When is the departure time for NY on Monday 26th?" may be considered similar because they instantiate the same underlying question ("What is the departure time for New York on Mondays?"), but not strictly equivalent, since the actual details (the dates) differ.

A successful approach to answer retrieval based on DQD addresses our desired improvements to the rule-based chatbot system. It improves the retrieval performance, since it results in more questions being successfully linked to their correct answer. Additionally, the tool can present the user with a set of candidate answers if it is not confident enough to select one.

Data preparation

From the original set of question-answer-label triplets, we produced a set of questionquestion (QQ) pairs labelled for semantic similarity. The transformation we applied to the data is equivalent to interpreting the result of the chatbot's retrieval heuristic in terms of DQD. Thus, all questions answered correctly by a particular answer make up a set of semantically similar questions; all questions answered incorrectly by a particular answer form pairs of semantically dissimilar questions with each of the questions for which that same answer is correct. In line with this interpretation, we generated QQ pairs as described below.

First, we grouped all the questions in our dataset by the answer they received. At this point, each answer is linked to a set of questions for which users have rated it as a good answer (its "positive" group), and to another set of questions for which it has been rated as bad (the answer's "negative" group). Second, we selected a subset of most rated (either as good or as bad) and most informative answers. We discarded very generic answers (e.g. greetings, thanks) and those stating the chatbot's inability to understand the question. An analysis of the distribution of answers in the dataset then revealed that, of the remaining 246 unique answers, the 40 answers with the highest number of total "positive" and "negative" questions made up 79% of the dataset overall, 73% of all "positive" questions, and 81% of all "negative" questions. Those 40 answers were the ones we selected for learning, since they are arguably the most useful ones: they are the most frequently given ones overall, and also comprise both the best-rated answers and the most heavily rejected by users. Next, for each of the 40 answers, we generated exhaustively: pairs of "positive" questions -these are pairs of semantically similar questions (according to our definition of semantic similarity); and pairs made up of one "positive" and one "negative" question -these are pairs of semantically dissimilar questions. Lastly, to keep the data for learning of manageable size, we sampled QQ pairs from the full pairings generated at the previous step. In order to avoid issues related to learning from an imbalanced dataset (there are more dissimilar than similar pairs), we took an equal number of similar and dissimilar pairs, by randomly sampling 10,000 similar pairs and 10,000 dissimilar pairs, which amounts to undersampling the majority class.

6 Experimental setup

Data preprocessing

Questions in our dataset share many features with SMS and with other types of usergenerated content, such as social media. The text is riddled with spelling mistakes (e.g. merci mais ca ne me precise pas le retard de marseille la reunion le 25 09 a 190h et j essai de vous appeler en vains car au bout de 15 mn ca racroche), but also with the deliberate use of simplifying and expressive devices [START_REF] Seddah | The french social media bank: a treebank of noisy user generated content[END_REF]: repeated punctuation, capitalisation, graphemic stretching, emoticons (e.g. merciiiiiiiii :) :), NON NON NON!!!!!!!!! J'ai besoin du numero de vol de CDG a JFK qui arrive ce soir).

For our task, the text of the questions in our QQ dataset underwent a number of cleaning and preprocessing steps. We cleaned up HTML markup and entities, and certain characters. Basic normalisation included lowercasing, removing punctuation, collapsing sequences of more than two repeated characters [START_REF] Accorsi | Seek&hide: Anonymising a french sms corpus using natural language processing techniques[END_REF], restoring elided vowels, standardising spelling variations of in-domain terms and proper names and merging those which are multi-word (e.g. ny, nyc, new york, and newyork all become newyork; AR, aller-retour, <>, etc. are all replaced by allerretour), and grouping sequences that match specific patterns under semantic and formal classes inspired from Bikel et al. [START_REF] Bikel | An algorithm that learns what's in a name[END_REF]: dates, telephone numbers, prices, measurements, other numeric expressions, URLs, email addresses, etc. Given the poor performance and strong disagreement of four language detection packages that we tried on our data, most probably due to the very short size of our questions, we abandoned the idea of automatically filtering out questions in a language other than French. We produced five versions of the text: 1. Preprocessed as described above. 2. Lemmatised using MElt [START_REF] Denis | Coupling an annotated corpus and a lexicon for state-of-the-art pos tagging[END_REF] on the preprocessed text. MElt is a maximum-entropy Markov-model POS tagger and lemmatiser with a normaliser/corrector wrapper trained on user-generated corpora annotated by hand. Some post-lemmatisation cleaning was needed, mainly for lemma disambiguation. 3. PoS: a version of the preprocessed text where tokens were replaced with their partof-speech tags as output by MElt. 4. Stemmed using Porter's algorithm on the lemmatised version. 5. Stemmed after removing accents from lemmas. Because customers use accents rather haphazardly, it seems reasonable to assume that reducing word forms to stems after stripping accents may decrease considerably the size of the vocabulary.

Baselines

Our weak baseline is the chatbot in its current form, taken as an (indirect) detector of similar questions. The construction procedure of our QQ dataset means that this baseline has 50% accuracy on our class-balanced data. Indeed, the chatbot correctly identifies all the similar QQ pairs as similar, but it also takes all the dissimilar ones for similar.

For the remainder of systems, including the second baseline, the same train/test split on the data was used, with an 80/20 ratio. We take as strong baseline the Jaccard similarity coefficient, a measure of overlap between sets which is common in information retrieval [START_REF] Manning | Introduction to information retrieval[END_REF], and which has been used for textual entailment recognition [START_REF] Malakasiotis | Learning textual entailment using svms and string similarity measures[END_REF] and for near-duplicate detection tasks [START_REF] Wu | Efficient near-duplicate detection for q&a forum[END_REF]. For each QQ pair, we compute the Jaccard coefficient between the two questions represented as a set of n-grams (with n running from 1 to 4). The cutoff value is optimised on the training set, and evaluated on the test set.

Proposed systems

The systems we are testing are two CNN architectures developed specifically for DQD, which performed very well on a dataset in English from the AskUbuntu forum [START_REF] Bogdanova | Detecting semantically equivalent questions in online user forums[END_REF]. CNN architectures have shown great success at a number of natural language processing tasks, such as classifying sentences [START_REF] Kim | Convolutional neural networks for sentence classification[END_REF] or modelling sentence pairs [START_REF] Yin | Abcnn: Attention-based convolutional neural network for modeling sentence pairs[END_REF].

CNN Our system is based on the CNN architecture for DQD proposed in [START_REF] Bogdanova | Detecting semantically equivalent questions in online user forums[END_REF]. First, the CNN obtains vector representations of the words, also known as word embeddings, from the two input segments. Next, a convolution layer constructs a vector representation for each of the two segments. Finally, the two representations are compared using cosine similarity. If the value of this metric exceeds an empirically estimated threshold, the two segments are classified as duplicate. The same feature maps (for word embedding and the convolution layer) are used to produce the representation of both questions.

Our CNN architecture is also inspired from [START_REF] Kim | Convolutional neural networks for sentence classification[END_REF]. The authors of that paper use the concatenation of several convolution filters with multiple feature widths. We improve the architecture proposed in [START_REF] Bogdanova | Detecting semantically equivalent questions in online user forums[END_REF] by changing the convolution layer to a set of convolution filters with multiple feature widths (cf. diagram in Figure 1).

The vector representation uses an embedding layer of 200 randomly initiated neurons which are trainable. Each convolution layer uses 100 neurons for the output of the filters, and the widths of the filters are 2, 3, and 5. The optimisation algorithm used for the network is stochastic gradient descent (SGD) with a learning rate of 0.005.

WR

CONV POOL cosine similarity Fig. 1. CNN architecture, with layers: word representation (WR) for a pair of questions (qn; output q W R n ); concatenated set of convolution filters (CONV); max pooling (POOL); question representation (r qn ); and cosine similarity measurement.

Hybrid deep CNN (CNN-Deep)

The second system we tested is described in detail in (selfReference). It combines a CNN similar to our first proposed system with a deep neural network with three hidden, fully-connected, layers, based on the architecture described in [START_REF] Afzal | Mayonlp at semeval-2016 task 1: Semantic textual similarity based on lexical semantic net and deep learning semantic model[END_REF]. A diagram of the system is shown in Figure 2.

The vector representation uses an embedding layer of 300 randomly initiated neurons which are trainable. The convolution layer uses 300 neurons for the output of filters with a kernel size of 15 units, and each deep layer has 50 neurons. The optimisation algorithm used for the network is SGD with a learning rate of 0.01.

WR

CONV POOL cosine similarity multi-layer deep network FC FC FC Fig. 2. CNN-Deep architecture: as the CNN, with the addition of fully-connected layers (FC).

Results and discussion

System performance

From Table 1, it is immediately obvious that two of the three systems (Jaccard and CNN-Deep) perform barely better than a random classifier (such as our weak baseline, which has accuracy of 50%), while CNN is at the order of 20 percentage points above both on four of the five text versions. The poor accuracy of the Jaccard similarity baseline goes to confirm that, for our task, word overlap is not a reliable indicator of semantic similarity. For example, the questions in pair 1 in Table 2 are similar (according to our definition) despite sharing almost no words (they share fewer words in French than in our English translation). On the other hand, CNN-Deep scoring barely better than the Jaccard baseline is consistent with the results reported in [START_REF] Rodrigues | Ways of asking and replying in duplicate question detection[END_REF] for a general-domain corpus. It is striking that, on this data and this task, a tool of this level of sophistication is on par with a very simple overlap measure. The complexity of CNN-Deep's architecture might be ill-suited to the needs of the task at hand. Conversely, the simpler CNN architecture performs better.

Although for each system the differences in accuracy when applied to the different versions of the text are generally small, each system seems to perform best on a specific version. Nonetheless, the three methods do not agree on which level of preprocessing is the most efficient: Jaccard seems to prefer lemmas over stems, while CNN-Deep does the worst on lemmas and the best on stems; and the performance of the CNN deteriorates with any additional processing on top of the initial preprocessing. Surprisingly enough, although stems from unaccented lemmas are more powerful in collapsing the vocabulary, they do not lead to improved performance compared to stems over lemmas as such. Representing the text exclusively as parts of speech has a negative impact on Jaccard and results in an even more marked drop in accuracy for CNN, but does not seem to affect CNN-Deep. Overall, it is hard to assess the benefit of the different types of text preprocessing. Depending on the tool and on the task, the effects may differ.

Difficulty of the task

The task we set out to tackle is hard. Two human annotators asked to label independently as semantically similar or not a random sample of 100 QQs pairs from our data have achieved a Cohen's kappa as low as 0.332. The annotators were given our definition of semantic similarity and a few examples (including a reply which is so general that it could arguably answer any query), and were instructed to decide whether the two questions in each pair are similar according to the definition. The agreement is very low not only between the annotators, but also between each of them and the ground truth. Very low correlation between raters has been reported in the literature for hard tasks. For instance, on a task that consisted in rating three aspects related to user satisfaction with the dialogue turns of an automated or human interlocutor, [START_REF] Higashinaka | Issues in predicting user satisfaction transitions in dialogues: Individual differences, evaluation criteria, and prediction models[END_REF] reports near-zero Spearman's rank correlation between two raters, including on the easiest of the three aspects, which is deciding if the interlocutor is a good listener or not. Such low agreement may suggest that the task is very hard for a human to solve, that the data may be too noisy for any patterns to be discoverable, or even that there may be no patterns to learn in the data in the first place. We believe the first hypothesis to be plausible in our case. The fact that our best system (CNN) achieves 60% accuracy on the same sample -which, while not as high as the performance on our test sets, is a considerable improvement over random label assignment -points to there being some actual patterns to learn from the data, even if they may not be easily discernible to a human judge.

Specification of the user's information need We believe the difficulty of deciding whether two questions are semantically similar according to our definition may stem from the complexity of correctly inferring the user's real information need from the question they ask. The potential discrepancy between a user's actual information need and what may be inferred from its expression in a textual query is a pervasive problem in information retrieval [START_REF] Manning | Introduction to information retrieval[END_REF]. As an example, to assess how well suited to a question the answers retrieved by their system were, the authors of [START_REF] Jijkoun | Retrieving answers from frequently asked questions pages on the web[END_REF] had raters "back-generate" a possible information need behind each question before judging the quality of the answers provided by the system. Those researchers point out that for some questions the assessors were unable to reconstruct the original information need, which means they were unable to judge the quality of the answers. Some of the questions in our dataset exhibit an underspecification of the information need (e.g. question 1 in example QQ pair 7), while others are extremely specific (e.g. question 2 in example QQ pair 7); further details are needed about the first one to decide whether the same answer could fit them both. Some questions are incomplete, as question 1 in our example pair 8; if we assume an information need (perhaps the most likely one, or perhaps Paris is the only destination reachable from the origin stated by the user), this question may be viewed as similar to question 2 in pair 8.

Annotators' knowledge of the domain and context Assessing the quality of answers in the domain at hand does not require any technical knowledge, so the "expert/novice" annotator distinction in [START_REF] Lowe | On the evaluation of dialogue systems with next utterance classification[END_REF] does not apply here sensu stricto; still, the level of familiarity with the domain (air carrier's products) may affect an annotator's perception of an answer's relevancy. Our annotators were not familiar with the domain, which complicates their assessment of whether the answer is specific enough to satisfy the query. Example QQ pair 2 in Table 2 shows two questions which may very well be acceptably answered by a reply providing comprehensive details about the company's seat reservation policy; however, the first user may expect a reply dealing specifically and exclusively with seat reservation when the deadline has expired. That goes for example pairs 3 and 4: as long as the generic answer is, in fact, exhaustive, it is perfectly valid for any question whose specific answer is included in the generic one. Human raters, however, will find it difficult to decide on the semantic similarity of two questions without some knowledge of the context and the domain. To decide whether the questions in example QQ pair 5 may be similar, one would need to know if both questions were asked a certain number of months earlier than the desired travel date, and if the company does have a policy for handling early bookings, in which case a common answer may satisfy both queries. Likewise, the semantic similarity of example pair 6 may hinge on the actual availability of a seat choice option; if there is none, this will be the valid answer to both questions. QQ pair 9 may be a case of similarity if the company's excess baggage policy is the same regardless of the route.

Conclusion and future work

Deciding whether two questions are semantically similar or not is a hard task for humans. Notwithstanding, one of the systems tested in this paper, the CNN, achieved good accuracy on a QQ set derived from user-chatbot exchanges labelled for user satisfaction, outperforming the rule-based chatbot on this task. By simply learning from userlabelled data collected over time, a chatbot can thus improve significantly its ability to detect similar questions in the course of time.

But ultimately, our goal is to assess the usefulness of DQD as part of an answerretrieving chatbot. Therefore, our next step will be to test our system on Step 2 (cf. Section 4), i.e. the actual retrieval of an answer using the output of Step 1 (DQD). To evaluate the performance of our proposed system on this task against the existing system as a baseline, we are preparing a set of questions labelled for their correct answer. Another issue to tackle will be an optimal way of performing fast and efficiently the comparisons between the incoming question and the ones in the reference set as that set grows over time.

In this experimental setup we have restricted ourselves to one-line dialogues, but conversations offer a good ground for yet another application of DQD: detecting the rephrasing of a question during a dialogue, which may be indicative of a problem that requires attention. It would also be interesting to assess the impact of more advanced spelling normalisation and correction on our best system's performance. In addition, new experiments could take account of the known correct answer to a past question when assessing its similarity with a new question. Last but not least, it will be interesting to validate the results reported here on a similar corpus coming from a different chatbot in a different domain.

Table 1 .

 1 Accuracy for the DQD task on the five versions of the data.

		Jaccard CNN CNN-Deep
	Preprocessed	52.8 74.9	55.8
	Lemmatised	55.2 72.5	53.0
	PoS	51.6 59.7	55.7
	Stemmed	54.3 72.4	56.0
	Stemmed unaccented lemmas 54.2 72.1	55.3

Table 2 .

 2 Example QQ pairs from our dataset (preprocessed version). The errors in the French are the users' (cf. Section 6.1), and the English translation mimics the French.

	Question 1	Question 2
	1 comment puis je choisir ma place dans le	je souhaiterai savoir comment faire pour
	avion	réserver un siège en ligne "i would like to
	"how can i choose my seat in the plane"	know how to book a seat online"
	2 je ai dépassé la date pour réservé un siège	bonsoir puis je réservé mon siege pour can-
	car je pars dans NUMBER jours comment	cun
	je peus faire	"hello can i book my seat for cancun"
	"i have missed the deadline for booking
	a seat because i leave in NUMBER days
	what can i do"
	3 est il possible de payer par chèque	peut on payer plusieurs mensualite
	"do you accept cheques"	"do you accept instalment payments"
	4 quels sont les moyens de paiement	est il possible de payer par paypal
	"what are the payment options"	"can i pay with paypal"
	5 je souhaiterai savoir pourquoi vous ne avez	bonjour je peut pas reserver pour avril
	pas de autres dates de disponible pour de-	"hello i cannot book for april"
	but septembre NUMBER
	"i would like to know why there are no other
	dates available for early september NUM-
	BER"
	6 a partir de quand puis je choisir mon siège	j ai reserve et je voudrais savoir ou je suis
	"when will i be able to choose my seat"	assise ou si je dois choisir ma place
		"i have booked a ticket and i would like
		to know where i am seated or if i need to
		choose my seat"
	7 poids	pour un deuxieme bagage vers les dom on a
	"weight"	droit a combien de kg
		"what is the maximum weight for a second
		piece of luggage for the overseas depart-
		ments"
	8 bonjour je suis à la réunion	cherche vol reunion charles de gaule
	"i am in réunion"	"looking for a reunion charles de gaule
		flight"
	9 je peux prendre le bagage sup sur le vol re-	bonjour	concernant	le	bagage
	tour ajout excédents	supplémentaire quel est le tarif vol
	"can i take the extra baggage on the flight	cdg neew york jfk classe éco
	back extra baggage"	"hello about the extra baggage what are the
		fees cdg neew york jfk economy flight"

Acknowledgements

This research is partly funded by the Regional Council of Brittany through an ARED grant. The present research was also partly supported by the CLARIN and ANI/3279/2016 grants. We are grateful to Telsi for providing the data.


RUMOUR AS A LITERARY DEVICE IN TACITUS

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

umours 1 are an essential tool in Tacitus' works; this has been generally accepted and their extremely high frequency speaks for itself; nevertheless, those rumores have been examined with great ambiguity. Systematic approaches are not frequent, 2 so that the many papers studying tacitean rumours provide a disintegrated picture. Moreover, researchers typically follow a purely content-based approach, which neglects syntactic and lexical variety of rumour and, sometimes, 3 drives them to misleading historical statements on the so-called reality of each rumour: did such or such fama really exist or did it come from Tacitus' inventiveness? First, this kind of discussion, usually based on the methods of source criticism, is very hard to settle, given the lack of evidence that we have on this particular question. 4 Then, the very high number of rumours in Tacitus' works makes it hard to believe that contemporary readers actually wondered about their reality.

But the most valuable argument why the tacitean rumour cannot be merely studied through a historical approach is that we cannot extract rumours from the tacitean narrative, i.e., from a strictly structured whole in which they are placed and which they contribute to organise: hence the historical approach consisting in taking them from Tacitus' text and observing their factuality seems irrelevant. This is what I will first focus on demonstrating; then, more broadly, I will define another positive process to combine literary analysis and historical conclusions, examining what tacitean rumours mean-and especially their form, that is, how Tacitus converts rumours from oral communication into literary content.

Before that, I should specify which kind of examples I will be using in this paper: indeed, scholars studying the tacitean rumours often avoid defining them, and therefore use our modern conception of rumour in their investigation. Yet, it seems that rumour in Rome, and especially during the Principate, was far more than mere gossip or uncertain and intrusive talks: it was rather a true medium of information and communication for the lower classes of the 1 I would like to thank Ellen O'Gorman for her availability in the organisation of this panel, and particularly for her wise counsel in the re-reading of this paper. 2 The only monograph is W. Ries' dissertation ( ), and it claims not to be systematic ( ).

3 See for instance Shatzman ( ) and ; Ferrero ; Newbold ( ) -and n. . 4 An observation made, for instance, by Cogitore ( ) . See also Syme ( ) -.

R society. 5 To stay away from the 'trap of anachronism', 6 I will count as rumour every anonymous, collective, public and unofficial discourse: in my opinion, those four characteristics have to be combined to fully define what a rumour was during the Empire.

I. Rumour and Transition: A Structural Technique

The main problem of the historical approach of rumours which consists in studying whether or not they have been invented by Tacitus is that it does not take account of the nature of the context they are part of. Rumours are included in the tacitean historiography, namely in a literary work, and they are partly used by Tacitus because their natural exuberance (according to him) could find expression through a particularly brilliant style in bravura pieces. In other words, the rumour is a device set up by Tacitus to meet Cicero's requirement that historia should be ornata. But this point has already been demonstrated by many scholars. 7 Furthermore, the rumour plays a significant role in the structuring and organisation of the historical narrative, especially in its transition points. Indeed, since Tacitus placed himself in the long tradition of annals, he was bound to certain conventions in the narrative's organisation, the most compelling one being perhaps the strict chronological order he had to follow, which led him to put next to each other events that were not closely connected by their geographical location or their subject. Those historiographical fetters could be partly removed either by smoothing the transitions of non-related facts or by recounting events thematically, thus breaking the chronological continuity. Both options are used by Tacitus, and in both cases the rumour is a useful tool for him.

I. Rumour in Spatial Transitions

In order to preserve the conventions of annals, the tacitean narrator has to observe the focus alternating between mainly two spheres, the domestic political situation (in Rome) and the external political situation, especially the wars against barbarians in the empire's outposts. This alternation seemed perhaps somewhat artificial to a contemporary reader, as it may have reminded them of the crudeness of the first annals, which Cicero criticised so severely. might be why Tacitus often describes some piece of news arriving in the city from the province when he needs to shift the view from one place to another. This way, the transition seems all the less an external necessity of narration since it was motivated by the course of news passing through the rumour, which Tacitus often calls fama when referring to it as a medium, that is, a channel of information. The very mobility of the rumour, a characteristic often spotted by Tacitus, its ability to cross the largest spaces, makes it a device most able to justify in the narrative itself transition points that would seem particularly abrupt if just assumed by the narrator.

If we focus on Tacitus' text, we will notice that the curt transition expression at Romae (common in Tacitus) is often softened by the description of the course of the rumour. This is very clear at the end of the second book of the Annals ( . -): after Germanicus' death, Piso has been trying to take over Syria and, once he has failed, surrenders to Cn. Sentius, who orders that he be taken to Rome. Then the narrative shifts to the Vrbs: non receptae condiciones <Pisonis>, nec aliud quam naves et tutum in urbem iter concessum est. ( ) at Romae, postquam Germanici valetudo percrebuit cunctaque ut ex longinquo aucta in deterius adferebantur, dolor ira et erumpebant questus.

The terms <of Piso> were not accepted and the only concessions made were a grant of ships and a safe-conduct to the capital. ( ) But at Rome, when the failure of Germanicus' health became current knowledge, and very circumstance was reported with the aggravations usual in news that has travelled far, all was grief and indignation. A storm of complaints burst out …9

Chapter , as we can see, ends with the naves and iter promised to Piso; yet the individual and tangible journey is replaced in the beginning of chapter by the course of rumour (in bold) and its arrival in Rome. Here, the rumour is not referred to with a noun, but with two verbs, the one passive (adferebantur), the other inchoative (percrebesco), a frequent way in Tacitus to refer to the growth and the spread of rumour. Furthermore, the tacitean narrator does not content himself with the bare transition expression at Romae, but softens the geographical opposition between east and west, province and Rome, with the adverbial clause postquam … adferebantur, which openly refers to the distance crossed by the rumour (ex longinquo). The very sociological features of the rumor, here its ability to cover long distances and its status of true medium for the people, 10 make it suitable to smooth the transition points, not only between provinces and Rome, as here, 11 but also whenever the narrative is divided between two places or two parties. 12

I. Rumour in Temporal Transitions

To reduce the artificiality of annalistic conventions, Tacitus also often groups together events related, but scattered in time, to give them unity in the narrative, which provides continuity and allows obvious dramatisation effects. This kind of manoeuvring is quite frequent, and Tacitus does not always clearly express it. Nevertheless when he does, rumour is often used to make this new organisation more plausible; and once again, the very sociological potentialities of the rumour, here its uncertainty, make possible this particular use. Indeed, rumour as a (popular) channel of news is, in Tacitus' aristocratic opinion, an uncertain medium, compared to the litterae or the nuntii. Rumor is essentially incertus (Hist. . ), and its unreliability is partly caused by the absence of an auctor clearly defined, which Cicero (Ad. Fam. . ) or Quintilian ( . ) also blamed, and is partly the sign of the control exerted by the power over information given to the vulgus. As Cassius Dio also points out ( . ), dissimulating information to the people is peculiar to the Roman Principate, and Galba, Tiberius and Livia give the most striking examples of this suppression. 13 This makes rumour a plausible device for some analepses set out by Tacitus to keep the thematic unity of such or such episode, since it did not seem unlikely that the people, because of this uncertain medium, would hear about events long after they really happened, and even after the princeps or the elite hear about them. This is why rumours often appear just before a flashback along with the use of the first person singular, through which the author openly announces an artificial layout of the historical material. It is particularly clear- 10 In contrast with the aristocratic media, mostly written and designed by terms like nuntius or litterae, as Achard has shown ( ) (for the vulgus militum), -and especially -(as a specificity of the imperial vulgus): '[les rumeurs] sont un moyen d'expression …; elles contrebalancent la force de l'écrit et du verbe des souverains'. See also Courrier ( ) -.

11 Similar use in Ann. . ; . (very clear); Hist. . . 12 This sort of spatial contrast can happen inside a province or a foreign country (see Ann. . ; . ; . ; . ; Hist. . ), and even inside Rome itself: for instance, the political and ideological struggle between Galba and Otho (Histories, book I) is encompassed in the opposition between Palatinus and Castra Praetoria, two spatial centres often linked by the course of rumour when the narrative moves from one place to another (see Hist. . for example). 13 For instance, see Livia's behaviour in Ann. . and . . cut in the first book of the Histories: if we put the events back to their chronological order 14 we will notice that the civil war of AD never looked like two successive duels (Galba/Otho then Otho/Vitellius), which we could believe reading Tacitus' account, but was from the beginning a war between three opponents (Galba/Otho/Vitellius), since Otho and Vitellius both rebelled against Galba at the beginning of January. It is clear that Tacitus first tells about the struggle between Galba and Otho, then about the one between Otho and Vitellius in order both to make the events more intelligible and to create a dramatic tension. The flashback on Vitellius' rebellion takes place quite artificially in chapter and is expressed through the author's personal voice, 15 but the true transition point, as Ries 16 has shown, is the long and pathetic rumour of the preceding chapter, which lies precisely in the centre of book I: trepidam urbem ac simul atrocitatem recentis sceleris, simul veteres Othonis mores paventem novus insuper de Vitellio nuntius exterruit, ante caedem Galbae suppressus … tum duos omnium mortalium impudicitia ignavia luxuria deterrimos velut ad perdendum imperium fataliter electos non senatus modo et eques … sed vulgus quoque palam maerere. Nec iam recentia saevae pacis exempla sed repetita bellorum civilium memoria captam totiens suis exercitibus urbem … nunc pro Othone an pro Vitellio in templa ituros? Rome was in a state of excitement and horror-stricken not only at the recent outrageous crime, but also at the thought of Otho's former character. Now it was terrified in addition by news with regard to Vitellius, which had been suppressed before Galba's death … Then the thought that two men, the worst in the world for their shamelessness, indolence and profligacy, had been apparently chosen by fate to ruin the empire, caused open grief not only to the senator and knights … but even to the common people. Their talk was no longer of the recent horrors of a bloody peace, but they recalled memories of the civil wars and spoke of the many times the city had been captured by Roman armies … but now-should they go to the temples to pray for an Otho or a Vitellius?

The people reacts to the nuntius of Vitellius' revolt, which was concealed before by Galba (ante caedem Galbae suppressus): indeed, we do find in the narrative of 14 Courbaud ( ) ff. 15 Nunc initia causasque motus Vitelliani expediam (Hist. . ). 16 Ries ( ) -.

chapters to references to this rebellion and to the news about it, 17 but they are in some way excluded from the main narrative as they are excluded from the people's knowledge. Through its uncertainty, this rumour makes plausible the flashback to the beginning of year and hence is used for its structural potentialities. Indeed, as one can notice reading it, it contains both references to the narrative of chapters to (trepidam urbem ac simul atrocitatem recentis sceleris, simul veteres Othonis mores pauentem …) and premonitions of the future, that is, of the struggle between Otho and Vitellius 18 (… duos omnium mortalium impudicitia ignavia luxuria deterrimos velut ad perdendum imperium fataliter electos … or nunc pro Othone an pro Vitellio in templa ituros?), thus making it the main subject of the second part of book I and of the beginning of book II. 19 What is crucial here is that this use of rumour as a transition device is absolutely not a random choice: Tacitus stresses the sociological features of the fama which could serve his purposes in the organisation of the narrative; its vivacity in geographical transitions; its unreliability in temporal transitions. If we might use the distinction of Genette (

), in both cases, the voice of the crowd is an intradiegetical relay of the extradiegetical voice of the narrator, sometimes also present. Being thus a structural technique both in the microand macrostructure of the narrative, rumour cannot be studied only through historical analysis; yet, our literary approach is not completely uncoupled with history, since those structural aspects are based upon the sociological features of the rumour. We must now try and determine a positive method to systematise how, through the tacitean rumour, history is embedded in literature and can be retrieved only through literary analysis.

II. From Syntactic Dependency to Political Dependency: Towards a Sociocritical Approach of the Tacitean Rumour

Indeed, if Tacitus aims to represent reality in his works, literature, to which ancient historiography undoubtedly belongs, does not simply reflect the social and historical universe as mere objective facts. Based on that observation, the sociocriticism is concerned with the way a text transcribes history into literature. 17 Hist. . , , , . 18 The two contenders are mentioned at the beginning and at the end of the passage in some sort of ring composition, as Ash ( ) -points out. 19 The same kind of time manipulation made plausible thanks to the rumour can be found when Tacitus introduces the Batavian revolt (Ann. . ), which started between August and November of but is reported as a whole only once it has reached some extent in the beginning of book IV (Courbaud ( ) -and n. ; Aubrion ( ) and n. ). See also (less developed): Ann. . ; . ; Hist. . , .

Edmond Cros (

) defines the theoretical aspects of this approach: 20 'Sociocriticism aims to bring out the relations existing between the structures of literary … work and the structures of the society in which this work is deeply rooted.' 21 In other words, sociocriticism is about finding, through the most literary and linguistic facts, reflections of the historical and social world. This theoretical approach has not been much applied to ancient literature, but in our opinion the tacitean rumour can be studied in this way.

Indeed, as I have already said, in my opinion, too little attention has been given to the very form of rumour; yet, if we observe it 'not at the level of content but at the level of forms', 22 we must be aware of a most interesting analogy: the rumour, which is, according to Tacitus, the people's medium of information and communication, incorporates in its syntactic features the political condition of the crowd in Rome during the early Principate. More specifically, when we look into the rumour from a narratological perspective, we notice that its connection to the narrative is ambiguous as it is both subordinate to the author's discourse (the diegesis) and trying, in some way, to free itself from that dependence; to us, this syntactic ambiguity transcribes within the text the similarly ambiguous relationship between the crowd and the holders of power in the Roman Empire. Let us now try and develop this analogy, which we could sum up in the following diagram: 20 See also Robin ( ) (in French), who summarises the main goals of the sociocriticism (or 'text sociology', 'sociologie du texte').

21 Cros ( ) . 22 Cros ( ) .

II. Dependence to the Main Discourse

From now on, we will be focusing only on rumours whose content is reported, as they seem to make audible in the history the voice of the crowd. Now this first affirmation must immediately be moderated: unlike the discourse of individuals, which can be reported either through direct or indirect speech, the discourse of the collective is always reported through indirect speech; in other words, the voice of the vulgus is essentially destined to be mediated, included in the narrator's voice. This syntactical fact is, in my opinion, also ideological: being included in the narrative, the people's voice has neither authority nor autonomy. Even in the rumours whose contents take up more space in the text, like the one criticising Augustus' reign in the beginning of the Annals, the link of subordination is clear through the use of accusative and infinitive (which is precisely a subordinate clause), so that we can say that this vox populi is strictly subordinate to the author's voice, unlike the discourse of some individual reported through direct speech and thus gaining credibility and authority.

If we turn back to the tools of sociocriticism, what does this linguistic assessment transcribe from the historical and social world Tacitus represents? To me, this subordinate position of the rumour inside the tacitean sentence transcribes the same analogical position of subordination of the people inside the Roman Empire, the plebs being the main group spreading and using rumours. Indeed, the -theoretical-power of the assembly of the people, which once ensured the stability of the Roman constitution according to Polybius and Cicero, had gradually disappeared during the first century BC, and Tacitus himself describes in the beginning of the Annals ( . ) how the crowd lost under Tiberius one of its oldest sign of sovereignty, its elective function. From this moment on, according to Tacitus, the vulgus was reduced to witnessing the politics conducted by the holders of power. Losing its institutional strength, it was placed under the domination of the elite.

This is what seems obvious on the first reading. Yet, the transcription system of Tacitus shall not be reduced to this sole static dimension, since both the status of the rumour as indirect speech and the political position of the crowd under the Principate are far more complex and dynamic.

II. Paradoxical Emancipation Towards the Tacitean Narrative

Now that we have highlighted the general aspect of the incorporation of the rumours in the tacitean narrative and its ideological implications, we must get to the specifics, that is, to the very forms of those reported rumores. Noteworthy is how many of these rumours have an unusual and surprising form, so that we might call them problematic indirect speeches: indeed, even though they are essentially subordinate to the main discourse of the narrative, they seem to break this position to attain some kind of independence thanks to some grammatical, syntactic and stylistic features, on which we will now focus.

First of all indeed, rumours, among other indirect speeches, include some terms that break the bounds of the indirect discourse. As reported speech, that is, a speech included in the narrative and absorbed by the narrator's voice, the rumour should not include deictics nor shifters, i.e., terms whose significance changes depending on the situation of utterance. Those are normally used only in direct speech. Yet, Utard has demonstrated how frequent those shifters were in Tacitus' indirect speech. 23 If we look more precisely at the rumours themselves, we observe that they include a great number of these 'syntactic anomalies': 24 the pronoun-adjective hic, 25 when the narrative (to whom belongs the rumour as any indirect speech) should only include ille; the deictic adverb nunc 26 instead of tum or tunc; the first person plural27 where we would expect the third person. These grammatical breaks (although not specific to the rumour but to some indirect speeches, as Utard shows) are the first features pointing out how the rumour tends to extend across its bounds as reported speech and to claim, within the narrative itself, some kind of autonomy.

This emancipation from the narrative in which it is normally enclosed may also take stylistic and syntactic forms more specific to the rumour. Indeed, the characteristics of these problematic speeches make them very close to direct speech, first because of their extent: the very length of some of these reported rumours might suspend the narrative as the direct speech of some orator would do. 28 Yet, beyond its mere extent, which is already symbolic, we must be sensitive to two points. First, there is no verb or substantive introducing the reported discourse, which causes the head-on meeting between narrative and discourse, as it would be the case between the narrative and some direct speech. This deletion is obvious for instance in the reported rumour bringing to the city news of Florus' and Sacrovir's rebellion (Ann. . ): At Rome, however, the tale ran that not the Treviri and Aedui only were in revolt, but the four-and-sixty tribes of Gaul: the Germans had joined the league, the Spains were wavering, and, as in all rumours, every statement was amplified and credited. The patriot, anxious for the common-wealth, grieved …

The rumour is here highlighted in bold. As we can see, there is absolutely no transition point between the narrative (at Romae then cuncta … credita) and the indirect speech: first, it seems to gain some independence, as if it were as independent as an direct speech; then (and in the very same motion), the rumour is being retied to the narrative through the accusative and infinitive clause, which, being grammatically a subordinate clause without any subordinating conjunction, expresses very well this ambiguous position. Besides, this formal ambiguity of the rumour is often rendered (especially in French) by the use of the free indirect speech when translated, that is, of a form of reported speech lying between direct and indirect speech 30 and most appropriate to express this problematic relation to the narrative. The other characteristic of these problematic indirect speeches, as we call them, is that they lack narrative relay inside them reintroducing the indirect speech and reminding the reader of its nature as reported discourse: 31 they have the appearance of a coherent whole, which could be extracted from the narrative and read separately, if it were not for the fact that they are syntactically not independent. A perfect example is the rumour against Augustus (Ann.

. -), which is both very long and almost without any reporting term. So, the result of this absence is the impression of autonomy, which is paradoxical since the very grammatical form of the rumour is a syntactic sign of subordination. Noteworthy is that, according to Utard's figures, reported in the following table, these almost independent speeches are both more usual in Tacitus than in any other ancient historians and significantly more frequent for the rumours than for the indirect speeches of individuals: In these examples, the rumour seems to emancipate itself into an autonomous and paradoxical discourse, almost similar to a direct speech. Yet, sometimes, the opposite move is also part of this emancipation process: indeed, in some ambiguous passages, it looks like the rumour is trying to merge with the narrative by breaking the subordination markers. The rumour spread by Germanicus' soldiers risen up in rebellion (Ann. . ) contains this kind of ambiguous clauses, which I have underlined, in contrast with indubitable indirect speech, which I have put in bold: … gemitusque ac planctus etiam militum auris oraque advertere: progrediuntur contuberniis. quis ille flebilis sonus? quod tam triste? feminas inlustris, non centurionem ad tutelam, non militem, nihil imperatoriae uxoris aut comitatus soliti: pergere ad Treviros [et] externae fidei.

The sobbing and wailing drew the ears and eyes of the troops themselves. They began to emerge from quarters: 'Why', they demanded, 'the sound of weeping? What calamity had happened? Here were these ladies of rank, and not a centurion to guard them, not a soldier, no sign of the usual escort or that this was the general's wife! They were bound for the Treviri-handed over to the protection of foreigners.'

In this rumour, no verb introduces the indirect speech, which is a first sign of autonomy; 32 but the most interesting point is that at first reading and before the indirect discourse become indubitable with the accusative feminas and even more indubitable with the infinitive pergere, the two interrogative clauses, which only include nominative and no verb revealing the mood used, should sound either like narrative if we imply the verb erat, or like direct speech with the verb est implied; thus in both cases like a full and independent discourse. Now Tac-itus could have chosen for those reported questions either to express the subjunctive (like sit or esset) or to resort to the accusative and infinitive, which is of frequent use in this case; but since he did not, those two sentences are given at first glance an ambiguous position in the narration which is muddying the waters between narrative and discourse. The same kind of blurring effects can be found in the rumour against Augustus (Ann. . , same font style): On the other side it was argued that 'filial duty and the critical position of the state had been used merely as a cloak … After that there had been undoubtedly peace, but peace with bloodshed-the disasters of Lollius and of Varus, the execution at Rome of a Varro, an Egnatius, an Iullus'. His domestic adventures were not spared: the abduction of Nero's wife, and the farcical question to the pontiffs, whether, with a child conceived but not yet born, she could legally wed; the debaucheries of Vedius Pollio; and, lastly, Livia-as a mother, a curse to the realm; as a stepmother, a curse to the house of the Caesars. 'He had left small room for the worship of heaven, when he claimed to be himself adored in temples and in the image of godhead by flamens and by priests! Here, just like in the other extract, the five nominative nouns (uxor, pontifices, luxus, mater, noverca) cause a breaking effect in the status of the discourse: since those clauses are part of the rumour, that is, of an indirect speech, we would expect to read just accusatives. Of course, a declarative verb in the passive voice might be implied, whose subjects would be the five substantives, as commentators have (timidly) speculated; be that as it may, the fact that Tacitus does not use accusative and infinitive clauses here does blur the position of those sentences towards the narrative and the indirect speech. In both cases, the bounds between reported speech and reporting discourse are briefly confused, and the voice of the crowd seems to attain for a moment some kind of syntactic autonomy.

So in many cases, we find in the rumours some attempt to reach a paradoxical independence towards the narrator's voice (i.e., the narrative), in which they should normally be completely included as indirect speeches; this emancipation is achieved thanks to the very ductility of the Latin and especially Tacitus' syntax. Now, using our sociocritical system of analogy, what do those problematic indirect speeches tell us about the populus Romanus during the early Principate? To me, it is the literary transcript of the popular effort to regain some political power. Indeed researchers in political history 33 have already emphasised that, although the evolution of the Roman constitution during the Principate took away from the people its institutional power, if any, and placed them under the domination of the elite, nevertheless, the people have found fields to exercise a new form of power over the political authorities, for instance by making non-political places (like the spectacula) new political places. Fama itself seems also to have sometimes turned into a political weapon. 34 Just like rumour's resistence to the domination of the narrative, the people, within the position of domination they endured, succeeded in finding new ways inside the system itself of regaining power with these parapolitical means, in a manner of speaking.

II. Rumour as a Sign of Disorder

Having more precisely defined this dynamic sociocritical system of analysis and specified this analogy between, on the one hand, rumours and narrative, and, on the other hand, crowd and political power, we might now wonder about its ideological insertion inside the narrative. The bare presence or absence of a fama does not mean anything particular for the political situation since some rumours are positive, 35 some negative, some spontaneous, some artificial; 36 but, 'at the level of the forms', 37 if we focus on reported rumours and the signs of syntactic instability they may contain, we might conclude that the greater their extent in the narrative, the more they overshadow the narrator's voice and the more they include those blurring effects we have defined earlier, the more they become a pure sign of disorder threatening the political stability. Put in other words: those problematic indirect speeches are symbolic of some sort of contempt of authority and order inside the tacitean sentence, which evokes the same risk inherent in the crowd, following Tacitus' opinion. It is obviously no coincidence that we read this kind of rumour spreading out inside the narrative when Tacitus describes episodes of lack of authority, where the political power is not firm and strong, 39 and hence does not sufficiently curb the crowd; 40 furthermore, to Tacitus, this instability is a constant threat towards imperial power, which his lexical choices illuminate, in the conspiracies narrative for instance as Cogitore has shown. 41 More precisely, the prince's absence from the city, 42 the moments of succession 43 or the mutinies 44 are, amongst others, circumstances favourable for those problematic indirect speeches. The whole extant Histories focus on the year AD, a period of civil war where four persons in two years became successively emperors, and thus a time which potentially lacked entrenched authority. If we turn back to Utard's figures (cf. the table earlier), we must notice that there are twice as many rumours in the Histories than in the Annals that are reported without reporting verbs, thus gaining some kind of stylistic independence, maybe because the latter are about a time with firmer authority thanks to the stability of the Julio-Claudian dynasty. This illustrates well the connection between syntactic and political disorder.

Rather than citing here every extract where we would find this sociocritical system of analogy between the lack of entrenched authority (a fact which belongs to history), the threat of political or social disorder (an impression which is the sign of Tacitus' own class ideology) and some sort of syntactic blur (a process which is peculiar to literature), we might just compare two contrasting excerpts which particularly illustrate our subject. In both texts, because of the 39 I do not agree with Aubrion's opinion ( ) , who claims that Tacitus uses rumours in the key moments of his narrative. Indeed, it is rather the opposite: those moments of lack of firm political power are, for this very reason, key moments of the history, and rumours are the (ideological) consequence of this absence of authority, rather than a mere rhetorical device. 40 The expression coercere vulgus or populum appears in Ann. . or . , for instance. 41 Amongst other passages, Cogitore ( ) . 42 See the reported rumours and the marks of syntactic instability they contain in Ann. . (both Tiberius and Drusus are not in Rome); Ann. . ( ) (Tiberius is away from the city); Hist. . (both Vespasian and Titus, who are also consuls, are absent from Rome). 43 See the many rumours in the beginning of the Annals (like the talks about Augustus in Ann. . -) and especially, more than their bare mention by the narrator, the remarkable extent of the indirect speech inside the narrative: the reported discourse take up more space than the story in five of the first ten chapters (chapters . , , , , and ).

44 Cf. Ann. . (and, more broadly, all the reported rumours during the mutinies of Annals, book I); Hist. . : this rumour is at the level of the form very 'independent' and at the level of content most daring towards Hordeonius Flaccus, whom Tacitus often describes as lacking of authority; those disrespectful words must be included in a succession of rumours (Hist. . ; . , , , , and ) that transcribe into the text the instability of Hordeonius' power over his soldiers. The historical consequence is to be found in Hist. . . circumstances, the political order is on the verge of being overthrown by the crowd; yet, the reactions of the authorities, namely the emperors Galba in the first one (Hist. . ), and Tiberius in the second (Ann. . ), favour in one case and restrict in the other the potential disorder. The presence of rumours in both extracts and, further, the way they are reported are very interesting: ignarus interim Galba et sacris intentus fatigabat alieni iam imperii deos, cum adfertur rumor rapi in castra incertum quem senatorem, mox Othonem esse qui raperetur, simul ex tota urbe, ut quisque obvius fuerat, alii formidine augentes, quidam minora vero, ne tum quidem obliti adulationis.

Galba in the meantime was in ignorance. Intent upon his sacrifices, he was importuning the gods of an empire which was already another's, when a report45 was brought to him that some senator or other was being hurried to the camp. Afterwards rumour said that it was Otho ; and at the same time people came from the whole city-some, who had happened to meet the procession, exaggerating the facts through terror, some making light of them, for they did not even then forget to flatter. nondum ea clades exoleverat cum ignis violentia urbem ultra solitum adfecit, deusto monte Caelio; feralemque annum ferebant et ominibus adversis susceptum principi consilium absentiae, qui mos vulgo, fortuita ad culpam trahentes, ni Caesar obviam isset tribuendo pecunias ex modo detrimenti. actaeque ei grates apud senatum ab inlustribus famaque apud populum, quia sine ambitione aut proximorum precibus ignotos etiam et ultro accitos munificentia iuverat.

The disaster had not yet faded from memory, when a fierce outbreak of fire affected the city to an unusual degree by burning down the Caelian Hill. 'It was a fatal year, and the sovereign's decision to absent himself had been adopted under an evil star'-so men began to remark, converting, as is the habit of the crowd, the fortuitous into the culpable, when the Caesar checked the critics by a distribution of money in proportion to loss sustained. Thanks were returned to him; in the senate, by the noble; in the streets, by the voice of the people: for without respect of persons, and without the intercession of relatives, he had aided with his liberality even unknown sufferers whom he had himself encouraged to apply.

In the first excerpt, Galba's lack of authority is transcribed into the text by the growing of the rumour (in bold) inside the sentence. Here, thanks to the syntax and the word order (see the hyperbaton), we start believing that the rumour is just mentioned (adfertur rumor); then a first complement is reported through indirect speech (rapi in castra incertum quem senatorem), then a second one with asyndeton specifies its content even more (mox Othonem esse), before a relative clause (qui raperetur) makes it even clearer: through this invasion of the rumour almost out of control inside the narrative, it is the growing disorder inside the city that is being illustrated.

In the second one, the indirect speech growing more and more independent (feralemque annum ferebant et ominibus adversis susceptum principi consilium absentiae, qui mos vulgo …) symbolises the same threatening disorder, since the reported discourse seems to challenge the author's voice. Yet, Tiberius' action, as often in Tacitus an endowment of money, calms the people; and very symbolically, the fama reappears in the next sentence (actaeque ei grates … fama apud populum), but this time perfectly included inside the narrative, since its content is not reported. The voice of the crowd has been here syntactically and politically silenced.

Conclusion

This paper aimed to refute the approach of the tacitean rumour as a mere historical fact whose reality could be either proven or denied; I hope that I have shown that rumours cannot be extracted from Tacitus' narrative which they are part of and participate in giving structure to. Yet, my purpose was somewhat broader: to me, historical and literary analyses have to be combined to truly understand Tacitus' representation of reality as a whole. It is the very sociological and historical features of the rumour that allow its use as a structural device, and studying this particular use, we have also made something of its phenomenology. Moreover, this combination of history and literature, of history through literature can be systematised thanks to the tools of the sociocriticism, which affirms that the structures of the society are to be found in the structures of the literary work: in my opinion, the ambiguous position of rumour as indirect speech in relation to the narrative is analogous to the paradoxical position of the crowd inside the political imperial system. In the ancient historians' works-and surely even today-history does not remain untouched and unmodified by the literature in which it is represented: it is turned into literary material and must be studied as such.

LOUIS AUTIN University Grenoble Alpes

lsautin@gmail.com

  dicebatur contra: pietatem erga parentem et tempora rei publicae obtentui sumpta … pacem sine dubio post haec, verum cruentam: Lollianas Varianasque clades, interfectos Romae Varrones, Egnatios, Iullos. nec domesticis abstinebatur: abducta Neroni uxor et consulti per ludibrium pontifices an concepto necdum edito partu rite nuberet; Vedii Pollionis luxus; postremo Livia gravis in rem publicam mater, gravis domui Caesarum noverca. nihil deorum honoribus relictum, cum se templis et effigie numinum per flamines et sacerdotes coli vellet.

Treviros modo et Aeduos sed quattuor et sex- aginta Galliarum civitates descivisse, adsumptos in socie- tatem Germanos, dubias Hispanias, cuncta

  29 , ut mos famae, in maius credita. optumus quisque rei publicae cura maerebat …

	23 Utard (	)	ff.
	24 Utard (	)	.
	25 Or the adverbs indicating space hic/huc/hinc/hac. See following passages: Ann. . -,
	; . ; . ; . ; Hist. . ; . , this last passage being maybe the most symbolic of this
	use thanks to the anaphora (Utard (	)	): Hanc esse Classici, hanc Tutoris patriam; horum
	scelere clausas caesasque legiones.

26 See Ann. . ; Hist. . and . . at Romae non

All texts and translations are taken from the Loeb Classical Library.

This nos can be found for instance in the rumours of Ann. . ; . ; . .

We might just consider the twenty-too clauses making up the rumour against Augustus in Ann. . .

This fama is also used as a structural device (cf. part I).

And which has surprised both Furneaux and Goodyear ('audacious introduction of oratio obliqua',).

Translation here is not quite exact, since the 'report' and the 'rumour' are designed by the same word in Latin (rumor).

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