Damien Sileo
email: damien.sileo@kuleuven.be

Tim Van De Cruys

Camille Pradel

Philippe Muller

A Pragmatics-Centered Evaluation Framework for Natural Language Understanding

New models for natural language understanding have recently made an unparalleled amount of progress, which has led some researchers to suggest that the models induce universal text representations. However, current benchmarks are predominantly targeting semantic phenomena; we make the case that pragmatics needs to take center stage in the evaluation of natural language understanding. We introduce PragmEval, a new benchmark for the evaluation of natural language understanding, that unites 11 pragmatics-focused evaluation datasets for English. PragmEval can be used as supplementary training data in a multi-task learning setup, and is publicly available, alongside the code for gathering and preprocessing the datasets. Using our evaluation suite, we show that natural language inference, a widely used pretraining task, does not result in genuinely universal representations, which presents a new challenge for multi-task learning.

Introduction

Over the last few years, pretrained for natural language understanding (NLU) have made a remarkable amount of progress on a number of widely accepted evaluation benchmarks. The GLUE benchmark [START_REF] Wang | GLUE: A multitask benchmark and analysis platform for natural language understanding[END_REF], for example, was designed to be a set of challenging NLU tasks, such as question answering, sentiment analysis, and textual entailment; yet, current state of the art systems surpass human performance estimates on the average score of its subtasks [START_REF] Yang | Xlnet: Generalized autoregressive pretraining for language understanding[END_REF]. Similarly, the NLU subtasks that are part of the Sent-Eval [START_REF] Conneau | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data[END_REF] framework, a widely used benchmark for the evaluation of sentence-tovector encoders, are successfully dealt with by current neural models, with scores that exceed the 90% mark. 1The results on these benchmarks are impressive, but sometimes lead to excessive optimism regarding the ability of current NLU models. For example, based on the resulting performance on the abovementioned benchmarks, a considerable number of researchers has even put forward the claim that their models induce universal representations [START_REF] Cer | Universal sentence encoder[END_REF][START_REF] Kiros | InferLite: Simple universal sentence representations from natural language inference data[END_REF][START_REF] Subramanian | Learning general purpose distributed sentence representations via large scale multi-task learning[END_REF][START_REF] Wieting | Towards universal paraphrastic sentence embeddings[END_REF][START_REF] Liu | Improving multi-task deep neural networks via knowledge distillation for natural language understanding[END_REF]. It is important to note, however, that benchmarks like SentEval and GLUE are primarily focusing on semantic aspects, i.e. the literal and uncontextualized content of text. While the semantics of language is without doubt an important aspect of language, we believe that a single focus on semantic aspects leads to an impoverished model of language. For a versatile model of language, other aspects of language, viz. pragmatic aspects, equally need to be taken into account. Pragmatics focuses on the larger context that surrounds a particular textual instance, and it is of vital importance for meaning representations that aspire to lay a claim to universality. Consider the following utterance :

(1) You're standing on my foot.

The utterance in (1) has a number of direct implications that are logically entailed, such as the implication that the hearer is standing on a body part of the speaker, or the implication that the speaker is touching the hearer. But there are also more indirect implications, that are not literally expressed, but need to be inferred from the context, such as the implication that the speaker wants the hearer to move away from them. The latter kind of implication, that is indirectly implied by the context of an utterance, is called implicature-a term coined by [START_REF] Grice | Logic and conversation[END_REF]. In real world applications, recognizing the implicatures of a statement is arguably more important than recognizing its mere semantic content.

The implicatures that are conveyed by an utterance are highly dependent on its illocutionary force [START_REF] Austin | How to do things with words[END_REF]. In Austin's framework, the lo-cution is the literal meaning of an utterance, while the illocution is the goal that the utterance tries to achieve. When we restrict the meaning of (1) to its locution, the utterance is reduced to the mere statement that the hearer is standing on the speaker's foot. However, when we also take its illocution into account, it becomes clear that the speaker actually formulates the request that the speaker step away. The utterance's illocution is clearly an important part of the entire meaning of the utterance, that is complementary to the literal content [START_REF] Green | Illocutionary force and semantic content[END_REF]. 2 The example above makes clear that pragmatics is a fundamental aspect of the meaning of an utterance. Semantics focuses on the literal content of utterances, but not on the kind of goal the speaker is trying to achieve. Pragmatics and discourse tasks focus on the actual use of language, so a pragmaticscentered evaluation could by construction be a better fit to evaluate how NLU models perform in practical use cases-and in any case it should at least be used as a complement to semantics-focused evaluation benchmarks. Ultimately, many use cases of NLP models are related to conversations with end users or analysis of structured documents. In such cases, discourse analysis (i.e. the ability to parse high-level textual structures that take into account the global context) is a prerequisite for human level performance. Moreover, standard benchmarks often strongly influence the evolution of NLU models, which means they should be as exhaustive as possible, and closely related to the models' end use cases.

In this work, we compile a list of eleven pragmaticsfocused tasks for English that are meant to complement existing benchmarks. We propose: (i) a new evaluation benchmark, named PragmEval, which is publicly available; 3 (ii) derivations of human accuracy estimates for some of the tasks; (iii) eval-2 In order to precisely determine their illocution, utterances have been categorized into classes called speech acts [START_REF] Searle | Speech act theory and pragmatics[END_REF], such as ASSERTION, QUESTION or ORDER which have different kinds of effects on the world. For instance, constative speech acts (e.g. the sky is blue) describe a state of the world and are either true or false while performative speech acts (e.g. I declare you husband and wife) can change the world upon utterance [START_REF] Austin | How to do things with words[END_REF].

3 https://github.com/ synapse-developpement/PragmEval and https://huggingface.co/datasets/ pragmeval uation on these tasks of a state of the art generalizable NLU model, viz. BERT (both with and without auxiliary finetunings); (iv) new comparisons of discourse-based and natural language inference based training signals, showing that the most widely used auxiliary finetuning dataset (MNLI) is not the best performing on PragmEval, which suggests a margin for improvement.

Related Work

Evaluation methods of NLU have been the object of heated debates since the proposal of the Turing Test. Automatic evaluations relying on annotated datasets are arguably limited but they have become customary practice. A popular method of evaluation is to predict sentence similarity [START_REF] Agirre | Semeval-2012 task 6: A pilot on semantic textual similarity[END_REF], leveraging human annotated scores of similarity between sentence pairs. This task requires some representation of the sentences' semantic content beyond their surface form, and sentence similarity estimation tasks can potentially encompass many aspects. However, it is not clear how human annotators weigh semantic, stylistic, and discursive aspects while rating. Using a set of more focused and clearly defined tasks has been another popular approach. [START_REF] Kiros | Skip-thought vectors[END_REF] proposed a set of tasks and tools for sentence understanding evaluation. These thirteen tasks were compiled in the SentEval [START_REF] Conneau | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data[END_REF] evaluation suite designed for automatic evaluation of pre-trained sentence embeddings. SentEval tasks are mostly based on sentiment analysis, semantic sentence similarity and natural language inference. Since SentEval evaluates sentence embeddings, the users have to provide a sentence encoder that is not fine-tuned during the evaluation. GLUE [START_REF] Wang | GLUE: A multitask benchmark and analysis platform for natural language understanding[END_REF] proposes to evaluate language understanding with less constraints than SentEval, allowing users not to rely on explicit sentence embedding based models. GLUE consists of nine classification or regression tasks that are carried out for sentences or sentence pairs. Three tasks focus on semantic similarity, and four tasks are based on NLI, which makes GLUE arguably semantics-based, even though it also includes sentiment classification [START_REF] Socher | Reasoning With Neural Tensor Networks for Knowledge Base Completion[END_REF] and grammaticality judgment [START_REF] Warstadt | Neural network acceptability judgments[END_REF]. NLI can be regarded as a universal framework for evaluation. In the Recast framework [START_REF] Poliak | Collecting diverse natural language inference problems for sentence representation evaluation[END_REF], existing datasets (e.g. sentiment analysis) are formulated as NLI tasks. For instance, based on the sentence don't waste your money, annotated as a negative review, they use handcrafted rules to generate the following example: (PREMISE: When asked about the product, Liam said "don't waste your money" , HYPOTHESIS: Liam didn't like the product, LABEL: entailment). However, the generated datasets do not allow to directly measure how well a model deals with the semantic phenomena present in the original dataset, since some sentences use artificially generated reported speech. Likewise, NLI data could be used to evaluate pragmatics and discourse analysis, but it is not clear how to generate examples that are not overly artificial. Moreover, it is unclear to what extent instances in existing NLI datasets need to deal with pragmatic aspects [START_REF] Bowman | Modeling natural language semantics in learned representations[END_REF]. SuperGLUE [START_REF] Wang | GLUE: A multitask benchmark and analysis platform for natural language understanding[END_REF] updates GLUE with six novel tasks that are selected to be even more challenging. Two of those tasks deal with contextualized lexical semantics, another two tasks are a form of question answering, and the remaining two are NLI problems. Only one of these NLI tasks, viz. CommitmentBank (de Marneffe et al., 2019), is related to pragmatics.

Another effort towards evaluation of general purpose NLP systems is DecaNLP [START_REF] Mccann | The natural language decathlon: Multitask learning as question answering[END_REF]. The ten tasks of this benchmark are all framed as question answering. For example, a question answering task is derived from a sentiment analysis task using artificial questions such as Is this sentence positive or negative? Four of these tasks deal with semantic parsing, and other tasks include NLI and sentiment analysis. Pragmatic phenomena can be involved in some tasks (e.g. the summarization task) although it is hard to assess to what extent.

Discourse relation prediction has punctually been used for sentence representation learning evaluation, by [START_REF] Nie | DisSent: Sentence Representation Learning from Explicit Discourse Relations[END_REF] and [START_REF] Sileo | Mining discourse markers for unsupervised sentence representation learning[END_REF], but they all used only one dataset (viz. PDTB; [START_REF] Prasad | The penn discourse treebank 2.0[END_REF], which we included in our benchmark. Discourse has also been considered for evaluation in the field of machine translation. [START_REF] Läubli | Has machine translation achieved human parity? a case for document-level evaluation[END_REF] showed that neural models achieve superhuman results on sentence-level translations but that current models yield underwhelming results when considering document-level translations, also making a case for discourse-aware evaluations. DiscoEval [START_REF] Chen | Evaluation benchmarks and learning criteria for discourse-aware sentence representations[END_REF] proposed a more principled evaluation of discourse modeling in NLP models. However, they mirror SentEval in that they rely on sentence embeddings and fixed compositions, which has been shown to be restrictive (Sileo et al., 2019a), and not necessarily in line with state of the art systems. Moreover, they focus on rather shallow aspects of document structure such as the position of sentences within a document. Other evaluations, such as linguistic probing or GLUE diagnostics [START_REF] Conneau | What you can cram into a single vector: Probing sentence embeddings for linguistic properties[END_REF][START_REF] Belinkov | Analysis methods in neural language processing: A survey[END_REF][START_REF] Wang | GLUE: A multitask benchmark and analysis platform for natural language understanding[END_REF] focus on an internal understanding of what is captured by the models (e.g. syntax, lexical content), rather than measuring performance on external tasks; this provides a complementary viewpoint, but it is outside the scope of this work.

PragmEval

Construction

Our goal is to compile a set of diverse pragmaticsrelated tasks. We restrict ourselves to classification either of sentences or sentence pairs, and only use publicly available datasets that are absent from other well-established benchmarks (such as Sent-Eval, GLUE, and SuperGLUE), in order to have complementary benchmarks. The scores in our tasks are not all meant to be compared to previous work, since we alter some datasets to yield more meaningful evaluations (we perform duplicate removal or class subsampling when mentioned). We found these operations necessary in order to leverage the rare classes and yield more meaningful scores. As an illustration, the GUM discourse corpus initially consists of more than 99% of unattached labels, and the dialog act annotations of the SwitchBoard conversation corpus contains 80% of statements. While disturbing the distributions of labels impacts the performance of models in real-world contexts, it seems reasonable when the goal is to indirectly evaluate the capacity of models to discriminate different semantic or pragmatic phenomena. Section 3.2. presents the tasks we selected, while 3.3. proposes a rudimentary taxonomy of how they address different aspects of meaning. A summary of the tasks, together with some examples, is also given in table 1.

Task overview PDTB

The Penn Discourse Tree Bank [START_REF] Prasad | Towards Full Text Shallow Discourse Relation Annotation : Experiments with Cross-Paragraph Implicit Relations in the PDTB[END_REF] contains a collection of fine-grained implicit (i.e. not signaled by a discourse marker) and explicit relations between sentences from the news domain in the Penn TreeBank 2.0, which signal the purpose of an utterance given a context utterance. Explicit relations can be easily predicted from the discourse marker alone [START_REF] Pitler | Easily identifiable discourse relations[END_REF] and are discarded. We select the level 2 relations, called types in PDTB terminology, as categories.

STAC [START_REF] Asher | Discourse structure and dialogue acts in multiparty dialogue: STAC corpus[END_REF] is a corpus of strategic chat conversations manually annotated with negotiation-related information, dialogue acts and discourse structures in the framework of Segmented Discourse Representation Theory [START_REF] Asher | Logics of conversation[END_REF]. We only consider pairwise relations between all dialogue acts, following [START_REF] Badene | Data programming for learning discourse structure[END_REF]. We remove duplicate pairs and dialogues that only have non-linguistic utterances (coming from the game server). We subsample dialogue act pairs with no relation so that they constitute 20% of each fold.

GUM [START_REF] Zeldes | The GUM corpus: Creating multilayer resources in the classroom[END_REF]) is a corpus of multilayer annotations for texts from various domains; it includes discourse structure annotations according to Rhetorical Structure Theory [START_REF] Mann | Rhetorical structure theory : a theory of text organization[END_REF]. Once again, we only consider pairwise interactions between discourse units (e.g. sentences/clauses). We subsample discourse units with no relation so that they constitute 20% of each document. We split the examples in train/test/dev sets randomly according to the document they belong to.

Emergent [START_REF] Ferreira | Emergent: a novel data-set for stance classification[END_REF]) is composed of pairs of assertions and titles of news articles that are against, for, or neutral with respect to the opinion of the assertion.

SwitchBoard [START_REF] Godfrey | Switchboard: Telephone speech corpus for research and development[END_REF] contains textual transcriptions of dialogues about various topics with annotated speech acts. We remove duplicate examples and subsample Statements and Non Statements so that they constitute 20% of the examples. We use a custom train/validation split (90/10 ratio) since our preprocessing leads to a drastic size reduction of the original development set. The label of a speech act can be dependent on the context (previous utterances), but we discarded it in this work for the sake of simplicity, even though integration of context could improve the scores [START_REF] Ribeiro | The influence of context on dialogue act recognition[END_REF].

MRDA [START_REF] Shriberg | The icsi meeting recorder dialog act (mrda) corpus[END_REF] contains textual transcriptions of multi-party real meetings, with speech act annotations. We remove duplicate examples. We use a custom train/validation split (90/10 ratio) since this deduplication leads to a drastic size reduction of the original development set, and we subsample Statement examples so that they constitute 20% of the dataset. We also discarded the context.

Persuasion [START_REF] Carlile | Give me more feedback: Annotating argument persuasiveness and related attributes in student essays[END_REF]) is a collection of arguments from student essays annotated with factors of persuasiveness with respect to a claim; considered factors are the following: Specificity, Eloquence, Relevance and Strength. For each graded target, we cast the ratings into three quantiles and discard the middle quantile.

SarcasmV2 [START_REF] Oraby | Creating and characterizing a diverse corpus of sarcasm in dialogue[END_REF] consists of messages from online forums with responses that may or may not be sarcastic according to human annotations.

Squinky dataset [START_REF] Lahiri | SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and Implicature[END_REF] gathers annotations on Formality, Informativeness, and Implicature, where sentences were graded on a scale from 1 to 7. The Implicature score is defined as the amount of information that is not explicitly expressed in a sentence. For each target, we cast the ratings into three quantiles and discard the middle quantile.

Verifiability

(Park and Cardie, 2014) is a collection of online user comments annotated as Verifiable-Experiential (verifiable and about writer's experience), Verifiable-Non-Experiential, or Unverifiable.

EmoBank [START_REF] Buechel | EmoBank: Studying the impact of annotation perspective and representation format on dimensional emo-tion analysis[END_REF] aggregates emotion annotations on texts from various domains using the VAD representation format. The authors define Valence as corresponding to the concept of polarity,4 Arousal as degree of calmness or excitement and Dominance as perceived degree of control over a situation. For each target, we cast the ratings into three quantiles and discard the middle quantile.

Taxonomy

It has been argued by [START_REF] Halliday | An Introduction to Functional Grammar[END_REF] that linguistic phenomena fall into three metafunctions: ideational for semantics, interpersonal for appeals to the hearer/reader, and textual for form-related aspects. This forms the basis of discourse relation types by [START_REF] Hovy | Parsimonious or profligate: How many and which discourse structure relations[END_REF], who call them semantic, interpersonal and presentational. PragmEval tasks cut across these categories, because some of the tasks integrate all aspects when they characterize the speech act or discourse relation category associated to a discourse unit (mostly sentences), an utterance, or a pair of these. However, most discourse relations involved focus on ideational aspects, which are thus complemented by tasks insisting on more interpersonal aspects (e.g. using appeal to emotions, or verifiable arguments) that help realize speech act intentions. Finally, intentions can achieve their goals with varying degrees of success. This leads us to a rudimentary grouping of our tasks:

A The speech act classification tasks (SwitchBoard, MRDA) deal with the detection of the intention of utterances. They use the same label set [START_REF] Core | Coding dialogs with the DAMSL annotation scheme[END_REF] but different domains and annotation guidelines. Similarly, a discourse relation characterizes how an utterance contributes to the coherence of a document/conversation (e.g through elaboration or contrast), so this task requires a form of understanding of the use of a sentence, and how a sentence fits with another sentence in a broader discourse. A discourse relation can be seen as a speech act whose definition is tied to a structured context [START_REF] Asher | Logics of conversation[END_REF]. Here, three tasks (PDTB, STAC, GUM) deal with discourse relation prediction with varying domains and formalisms. 5 The Stance detection task can be seen as a coarsegrained discourse relation classification.

5 These formalisms have different assumptions about the nature of discourse structure.

B Detecting emotional content, verifiability, formality, informativeness or sarcasm is necessary in order to figure out in what realm communication is occurring. A statement can be persuasive, yet poorly informative and unverifiable. Emotions [START_REF] Dolan | Emotion, cognition, and behavior[END_REF] and power perception [START_REF] Pfeffer | Understanding the role of power in decision making[END_REF] can have a strong influence on human behavior and text interpretation. Manipulating emotions can be the main purpose of a speech act as well. Sarcasm is another means of communication and sarcasm detection is in itself a suitable task for the evaluation of pragmatics, since sarcasm is a clear case of literal meaning being different from the intended meaning.

C Persuasiveness prediction is a useful tool to assess whether a model can measure how well a sentence can achieve its intended goal. This aspect is orthogonal to the determination of the goal itself, and is arguably equally important.

Note that the semantic tasks of GLUE can also be considered as a grouping of tasks, where the goal is to represent accurately the denotation of utterances (e.g. the identity of the objects and agents they involve, the relation between them, the temporal and spatial location). In contrast, solving Pragm-Eval tasks requires knowledge of complementary aspects that characterize utterances in a different way. The A group characterizes the kind of frame into which semantic content fits; for instance, identical subjects, verbs, and objects can be used in a question, a claim, or an instruction. Semantic tasks (semantic similarity, NLI) usually compare utterances within the same frame. Additionally, utterances with the same semantic content can differ according to aspects involved in group B and C, e.g. formality or persuasiveness. To ensure that these aspects are taken into account by NLU models, a pragmatic evaluation is required.

Evaluation

Models

Our goal is to assess the performance of popular NLU models and the influence of various training signals on PragmEval scores. We evaluate state of the art models and baselines on PragmEval using the Jiant framework [START_REF] Wang | jiant 1.2: A software toolkit for research on general-purpose text understanding models[END_REF]. Our baselines consist of an average of GloVe [START_REF] Pennington | GloVe: Global Vectors for Word Representation[END_REF] embeddings (CBoW), and a BiLSTM with both GloVe and ELMo [START_REF] Peters | Deep contextualized word representations[END_REF] embeddings. We equally evaluate BERT [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF] base uncased models, and perform experiments with Supplementary Training on Intermediate Labeled-data Tasks [START_REF] Phang | Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks[END_REF].

STILT is a further pretraining step on a data-rich task before the final fine-tuning evaluation on the target task. STILTs can be combined using multitask learning. We use Jiant's default parameters,6 and uniform loss weighting when multitasking (a different task is optimized at each training batch). We finetune BERT with four of such training signals:

MNLI [START_REF] Williams | A broad-coverage challenge corpus for sentence understanding through inference[END_REF] is a collection of 433k sentence pairs manually annotated with contradiction, entailment, or neutral relations. [START_REF] Phang | Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks[END_REF] showed that finetuning with this dataset leads to accuracy improvement on all GLUE tasks except CoLA [START_REF] Warstadt | Neural network acceptability judgments[END_REF].

DisSent [START_REF] Nie | DisSent: Sentence Representation Learning from Explicit Discourse Relations[END_REF] consists of 4.7M sentence pairs that are separated by a discourse marker (from a list of 15 markers). Prediction of discourse markers based on the context clauses/sentences with which they occur has been used as a training signal for sentence representation learning. The authors used handcrafted rules for each marker in order to ensure that the markers signal an actual relation. DisSent has underwhelming results on the GLUE tasks as a STILT [START_REF] Wang | Can you tell me how to get past sesame street? sentence-level pretraining beyond language modeling[END_REF].

Discovery [START_REF] Sileo | Mining discourse markers for unsupervised sentence representation learning[END_REF] is another dataset for discourse marker prediction, composed of 174 discourse markers with 10k usage examples for each marker. Sentence pairs were extracted from web data, and the markers come either from the PDTB or from a heuristic automatic extraction.

PragmEval refers to all PragmEval tasks used in a multitask setup; since we use a uniform loss weighting, we discard Persuasion classes other than Strength (note that the other classes can be considered subfactors for strength) in order to prevent the Persuasion task to overwhelm the others.

Human accuracy estimates

For a more insightful comparison, we propose derivations of human accuracy estimates for the datasets we used. The authors of Sar-casmV2 [START_REF] Oraby | Creating and characterizing a diverse corpus of sarcasm in dialogue[END_REF] dataset directly report 80% annotator accuracy compared to the gold standard. [START_REF] Prasad | Towards Full Text Shallow Discourse Relation Annotation : Experiments with Cross-Paragraph Implicit Relations in the PDTB[END_REF] report 84% annotator agreement for PDTB 2.0, which is a lower bound of accuracy. For GUM [START_REF] Zeldes | The GUM corpus: Creating multilayer resources in the classroom[END_REF], an attachment accuracy of 87.22% and labelling accuracy of 86.58% as compared to the 'gold standard' after instructor adjudication is reported. We interleaved attachment and labelling in our task. Assuming human annotators never predict the non-attached relation, 69.3% is a lower bound for human accuracy. Authors of the Verifiability [START_REF] Park | Identifying appropriate support for propositions in online user comments[END_REF] dataset report an agreement κ = 0.73 which yields an agreement of 87% given the class distribution, which is a lower bound of human accuracy. We estimated human accuracy on EmoBank [START_REF] Buechel | EmoBank: Studying the impact of annotation perspective and representation format on dimensional emo-tion analysis[END_REF] with the intermediate datasets provided by the authors. For each target (V,A,D) we compute the average standard deviation, and compute the probability (under normality assumption) of each example rating of falling under the wrong category.

Unlike the GLUE benchmark (Nangia and Bowman, 2019), we do not yet provide human accuracy estimates obtained in a standardized way. The high number of classes would make that process rather more difficult. But our estimates are still useful even though they should be taken with a grain of salt.

Overall results

Task-wise results are presented in table 2. We report the average scores of 6 runs of STILT and finetuning phases. PragmEval seems to be challenging even for the BERT base model, which has shown strong performance on GLUE (and vastly outperforms the [START_REF] Bai | Deep enhanced representation for implicit discourse relation recognition[END_REF], Emergent score is from [START_REF] Ferreira | Emergent: a novel data-set for stance classification[END_REF] and Verifiability score is derived from [START_REF] Park | Identifying appropriate support for propositions in online user comments[END_REF].

baselines on our tasks). For many tasks, there is a STILT that significantly improves the accuracy of BERT. The gap between human accuracy and BERT is particularly high on implicit discourse relation prediction (both for the PDTB corpus and the GUM RST corpus). This task is known to be difficult, and previous work has also shown that task dedicated models are not yet on par with human performance either on PDTB [START_REF] Bai | Deep enhanced representation for implicit discourse relation recognition[END_REF] or RST data [START_REF] Morey | How much progress have we made on RST discourse parsing? a replication study of recent results on the RST-DT[END_REF].

Pretraining on MNLI does not improve the Pragm-Eval average score for the BERT base model. A lower sarcasm detection score could indicate that BERT+MNLI is more focused on the literal content of statements, even though no STILT improves sarcasm detection. All models score below human accuracies, with the exception of emotion classification (but it is only due to the valence prediction subtask). [START_REF] Phang | Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks[END_REF] and SentEval [START_REF] Conneau | Supervised Learning of Universal Sentence Representations from Natural Language Inference Data[END_REF]. However, our evaluation suggests that finetuning a model with MNLI alone has significant drawbacks.

More detailed results for datasets with several subtasks are shown in table 4. We note that MNLI STILT significantly decreases relevance estimation performance (on BERT base and while multitasking with DisSent). Many models surpass the human estimate at valence prediction, a well studied task, but interestingly this is not the case for Arousal and Dominance prediction.

The categories of our benchmark tasks cover a broad range of pragmatic aspects. The overall accuracies only show a synthetic view of the tasks evaluated in PragmEval. Some datasets contain many subcategories that allow for a fine grained analysis through a wide array of classes (e.g. 51 categories for MRDA). sibly close to the neutral class in NLI), which is useful but not sufficient for pragmatics understanding. The STILTs have complementary strengths even with given tasks, which can explain why combining them is helpful. However, we used a rather simplistic multitask setup, and efficient combination of the tasks remains an open problem.

Conclusion

We proposed PragmEval, a set of pragmatics related evaluation tasks, and used them to evaluate BERT finetuned on various auxiliary finetuning tasks. The results lead us to rethink the efficiency of mainly using NLI as an auxiliary training task. PragmEval can be used for training or evaluating NLU or pragmatics related work in general. Much effort has been devoted to NLI for training and evaluation for general purpose sentence understanding, but we just scratched the surface of the use of pragmatics oriented tasks. In further investigations, we plan to use more general tasks than classification on sentences or sentence pairs, such as longer and possibly structured sequences. Several of the datasets we used (MRDA, SwitchBoard, GUM, STAC) already contain such higher level structures.

Of course defining a generic architecture for structured tasks in which to evaluate the contribution of trained representations is not straightforward. In addition, a more inclusive comparison with human annotators on pragmatics tasks could also help to pinpoint the weaknesses of current models dealing with pragmatics phenomena. Yet another step would be to study the correlations between performance metrics in deployed NLU systems and scores of the automated evaluation benchmarks (GLUE/PragmEval) in order to validate our claims about the centrality of pragmatics.

Acknowledgements

This work is part of the CALCULUS project, which is funded by the ERC Advanced Grant H2020-ERC-2017. ADG 788506

Table 1 :

 1 Example instances for each of the PragmEval tasks (these examples were selected for their conciseness and are not representative of the whole dataset)

	Dataset	Example

Table 2 :

 2 Transfer test scores across PragmEval tasks; we report the average when the dataset has several classification tasks (as in Squinky, EmoBank and Persuasion); B(ERT)+X refers to BERT pretrained classification model after an auxiliary finetuning phase on task X . All scores are accuracy scores except SwitchBoard/MRDA, which are macro-F1 scores. Previous work refers to the best scores from previous work that used a similar setup, where PDTB score is from

		PDTB STAC GUM Emergent SwitchB. MRDA Persuasion Sarcasm Squinky Verif. EmoBank
	CBoW	27.4 32	20.5 59.7	3.8	0.7	70.6	61.1	75.5	74.0 64.0
	BiLSTM	25.9 27.7 18.5 45.6	3.7	0.7	62.6	63.1	72.1	74.0 63.5
	BiLSTM+ELMo	27.5 33.5 18.9 55.2	3.7	0.7	67.4	68.9	82.5	74.0 66.9
	Previous work	48.2 -	-	73.1	-	-	-	-	-	81.1 -
	BERT	48.8 48.2 40.9 79.2	38.8	22.3	74.8	77.1	87.5	86.7 76.2
	BERT+MNLI	49.1 49.1 42.8 81.2	38.1	22.7	71.7	73.4	88.2	86.0 76.3
	BERT+PragmEval 49.1 57.1 42.8 80.2	40.3	23.1	76.2	75.0	87.6	85.9 76.0
	BERT+DisSent	49.4 49.0 43.9 79.8	39.2	22.0	74.7	74.9	87.5	85.9 76.2
	B+DisSent+MNLI 49.6 49.2 44.2 80.9	39.8	22.1	74.0	74.1	87.6	85.6 76.4
	BERT+Discovery	50.7 49.5 42.7 81.7	39.5	22.4	71.6	76.7	88.6	86.3 76.6
	B+Discovery+MNLI 51.3 49.4 43.1 80.7	40.3	22.2	73.6	75.1	88.9	86.8 76.0
	Human estimate	84.0 -	69.3 -	-	-	-	80.0	-	87.0 73.1

Table 3

 3

	shows aggregate results alongside com-
	parisons with GLUE scores. The best overall un-
	supervised result (GLUE+PragmEval average) is
	achieved with Discovery STILT. Combining Dis-
	covery and MNLI yields both a high PragmEval
	and GLUE score, and also yields a high GLUE
	diagnostics score. All discourse based STILTs im-
	prove GLUE score, while MNLI does not improve
	PragmEval average score. PragmEval tasks based
	on sentence pairs seem to account for the variance

Table 3 :

 3 Table 5 in appendix A shows a fine grained evaluation which yields some insights on the capabilities of BERT. We report the 5 most frequent classes per task. It is worth noting that the BERT models do not neglect rare classes. These detailed results reveal that BERT+MNLI scores for discourse relation prediction are inflated by good scores on predicting the absence of relation (pos-PragmEval AV G P.E.-Pairs AV G P.E.-Single AV G GLUE AV G GLUE diagnostics Aggregated transfer test accuracies across PragmEval and comparison with GLUE validation downstream and diagnostic tasks (GLUE diagnostic tasks evaluate NLI performance under presence of linguistic phenomena such as negation, quantification, use of common sense); BERT+X refers to BERT pretrained classification model after auxiliary finetuning phase on task X ; P.E.-Pairs AV G is the average of PragmEval sentence pair classification tasks.

	BERT		61.8±.4	57.9±.5	62.3±.3	74.7±.2	31.7±.3
	BERT+MNLI		61.7±.5	57.2±.5	62.2±.4	77.0±.2	32.5±.6
	BERT+PragmEval MTL 63.0±.4	60.0±.4	62.6±.2	75.3±.2	31.6±.3
	BERT+DisSent	62.0±.4	58.4±.4	62.2±.3	75.1±.2	31.5±.3
	B+DisSent+MNLI	62.1±.4	58.2±.4	62.3±.2	76.6±.1	32.4±.0
	BERT+Discovery	62.4±.3	58.2±.4	62.7±.3	75.0±.2	31.3±.2
	B+Discovery+MNLI	62.5±.4	58.5±.5	62.8±.3	76.6±.2	33.3±.2
			Persuasiveness		EmoBank		Squinky
		Eloquence Relevance Specificity Strength Valence Arousal Dom. Inf. Implicature Formality
	BERT	75.6	63.5	81.6	78.3	87.1	72.0	69.5 92.2 72.1	98.3
	BERT+MNLI	74.7	57.5	82.3	72.2	86.6	72.4	69.9 92.5 73.9	98.1
	BERT+PragmEval 75.6	64.0	83.2	82.0	86.8	71.9	69.2 92.3 71.8	98.6
	BERT+DisSent	73.8	63.0	82.6	79.5	87.1	71.4	70.1 92.6 72.0	97.7
	B+DisSent+MNLI 76.9	61.5	83.9	73.9	87.6	72.1	69.4 91.5 73.4	97.9
	BERT+Discovery	76.0	59.1	80.1	71.4	86.8	72.6	70.5 93.2 74.2	98.5
	B+Discovery+MNLI 74.1	60.4	79.4	80.4	86.4	72.1	69.6 93.1 75.3	98.4
	Human estimate	-	-	-	-	74.9	73.8	70.5 -	-	-

Table 4 :

 4 Transfer test accuracies across PragmEval subtasks (Persuasiveness, EmoBank, Squinky) BERT+X refers to BERT pretrained classification model after auxiliary finetuning phase on task X .

  7 . Philippe Muller is partially supported by the ANR (ANR-19-PI3A-0004) through the AI Interdisci-plinary Institute, ANITI, as a part of France's "Investing for the Future -PIA" program. He is also part of the programme DesCartes and is supported by the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.

http://nlpprogress.com/english/

This is the dimension that is widely used in sentiment analysis.

https://github.com/nyu-mll/jiant/ jiant/config/examples/stilts_example. conf

https://calculus-project.eu/