Fabien Lopez
email: fabien.lopez@univ-grenoble-alpes.fr

Gabriela Gonzalez-Saez

Damien Hansen

Mariam Nakhle

Behnoosh Namdarzadeh

Marco Dinarelli

Emmanuelle Esperança-Rodier

Sui He

Sadaf Mohseni

Caroline Rossi

Didier Schwab

Jun Yang

Jean-Baptiste Yunès

Lichao Zhu

Nicolas Ballier

The MAKE-NMTViz System Description for the WMT23 Literary Task

.

 was trained for 10 epochs using GeneralData, Test2, and Valid2; (ii) second, we fine-tuned such Transformer using documentlevel data, with 3-sentence concatenation as context, for 4 epochs using Train, Test1, and Valid1 data. We then compared the three translation outputs from an interdisciplinary perspective, investigating some of the effects of sentence-vs. document-based training. Computer scientists, translators and corpus linguists discussed the remaining linguistic issues for this discourse-level literary translation.

Introduction

In order to analyse literary translations, we have gathered an interdisciplinary team of translators, linguists and computational scientists. We used this opportunity to explore neural machine translation of literary texts as a test set for test suites and unsolved issues for MMT literary translations, especially for the Chinese-English language pair. While the topic of literary machine translation has gained momentum in the last years, there have still been few attempts to customize systems to liter-ary data, although this idea is also drawing attention (Kenny and Winters, forthcoming). Indeed, research has been carried out on this subject, notably on Catalan [START_REF] Toral | What level of quality can neural machine translation attain on literary text[END_REF], but also on Slovenian [START_REF] Kuzman | Neural Machine Translation of Literary Texts from English to Slovene[END_REF], German and Russian [START_REF] Matusov | The Challenges of Using Neural Machine Translation for Literature[END_REF], and on French [START_REF] Baayen | lan-guageR: Analyzing Linguistic Data: A Practical Introduction to Statistics. R package version 1[END_REF], where research suggests that MT systems can be further fine-tuned on specific genres and individual translator styles (Hansen and Esperança-Rodier, V.2023).

Of course, these very attempts bring about many issues concerning textual ownership, copyright, translator status and livelihood, possibly lowered quality, cognitive friction, etc. [START_REF] Taivalkoski-Shilov | Ethical Issues Regarding Machine(-Assisted) Translation of Literary Texts[END_REF]. It is therefore important to include these ethical aspects into the research and clarify its objectives: for instance, whether MT should serve as a reading aid [START_REF] Oliver | InLéctor: Automatic Creation of Bilingual E-Books[END_REF], or as a postediting tool that may decrease the effort needed to translate [START_REF] Kolb | Less room for engagement[END_REF] and constrain creativity [START_REF] Guerberof | Creativity in Translation: Machine Translation as a Constraint for Literary Texts[END_REF].

Part research has also focused on evaluating the use of existing tools for literary texts. In the context of Chinese to English, attention has been paid to some of the specific shortcomings of MT systems, such as the translation of adjectival possessive pronouns [START_REF] Jiang | A Contrastive Study on the Rendition of Adjectival Possessive Pronouns in Pride and Prejudice by Human Translation and Online Machine Translation[END_REF], or theme-rheme progressions [START_REF] Jiang | How are neural machinetranslated chinese-to-english short stories constructed and cohered? an exploratory study based on themerheme structure[END_REF]. Such limitations can indeed have a drastic impact on readers' acceptance, which [START_REF] Shih | Can Machine Translation Declare a New Realm of Service? Online Folktales as a Case Study[END_REF] explores in the context of online folktales, confirming that the text's function plays a large role in this respect.

Lastly, [START_REF] Thai | Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature[END_REF] have also pointed the incompatibility of MT metrics, document-level or otherwise, for literary texts, concluding that "hu-man expert evaluation is currently the only way to judge the quality of literary MT".

The rest of the paper is organised as follows: Section 2 details our approaches to the task and the training data of our experiments, Section 3 presents the results and Section 4 discusses them.

Data and Tools Used

This section details the toolkits we used and our training data for the three submissions authorised for the task. We first used part of the training data proposed by the organisers (Wang et al., 2023a) to observe the translations from mBART50 from Chinese into English before fine-tuning mBART (primary submission). We then used a fine-tuned context-aware concatenation-based Transformer trained at document level (contrastive1 submission) and a traditional sentence-level Transformer (con-trastive2 submission).

Primary model: mBART50 fine-tuning

As a primary submission, we used GuoFeng corpus (Wang et al., 2023a) to fine-tune the mBART50 model with Chinese-English data, using the Train set for training, Test1 as test set, and Valid1 as validation set. We followed similar training parameters to [START_REF] Lee | Pretrained multilingual sequence-to-sequence models: A hope for low-resource language translation[END_REF] when fine-tuning mBART50. As [START_REF] Lee | Pretrained multilingual sequence-to-sequence models: A hope for low-resource language translation[END_REF], we trained for 3 epochs, using gelu as an activation function, with a learning rate of 0.05, dropout of 0.1 and a batch size of 16 (we parallelised two A100 GPUs with batch size 8 per device). We decoded using a beam search of size 5.

Contrastive models

We submit two contrastive models, the first is a context-aware model (contrastive1) built on the second system, a sentence-level model (contrastive2).

For our contrastive1 submission, we used a context-aware NMT system based on the concatenation method [START_REF] Lupo | Encoding sentence position in context-aware neural machine translation with concatenation[END_REF]. The training was performed in two steps: (i) a sentence-level transformer [START_REF] Vaswani | Attention is all you need[END_REF] was trained for 10 epochs 1 using General Data as train set, Test2 as test set, and Valid2 as validation set ; (ii) second, we fine-tuned at document-level using 3-sentence concatenation for 4 epochs 2 using Train as train set, Valid1 as validation set and Test1 as test set. During the fine-tuning, we used ReLU as an activation function, with an inverse square root learning 1 We used only 10 epochs because of time constraints 2 We used only 4 epochs because of time constraints rate decay, dropout of 0.1, and a batch size of 64. We decoded using a beam search of size 4. For our contrastive2, we used the model trained at step (i) (sentence-level). The training parameters were an inverse square root learning rate decay, a dropout of 0.1, and a batch size of 64. We decoded using a beam search of size 4.

Evaluation Metrics

To evaluate our models, we use the BLEU score metric [START_REF] Papineni | Bleu: a method for automatic evaluation of machine translation[END_REF] as implemented in the Moses package.

We performed a human annotation of errors in the translation obtained by our primary submission. 109 segments were selected and annotated by three evaluators that are Chinese native speakers. To measure the inter-annotator agreement, we used Fleiss' kappa [START_REF] Fleiss | Measuring nominal scale agreement among many raters[END_REF]. The score is calculated to measure the inter-rater reliability of the annotations as the following equation

κ = P o -P e 1 -P e
where P o -P e measures the real concordance of annotations that are not achieved above chance, while 1 -P e measures the achievable concordance of annotations above chance. In our case, we computed errors by type as well as error types by segment (6 types and 109 segments, cf4.3.2).

Experiments and Results

We provide a human analysis of the primary model by discussing the improvements observed with the mBART fine-tuning with respect to the baseline. Additionally, we report the BLEU scores of our three systems.

Baseline of primary model: mBART50

During the training phase of the competition, with the standard HuggingFace implementation of mBART50, we observed the following issues when we translated Test1 from Chinese to English, which was part of the data provided for training by the organisers:

• hallucinations

• discrepancy between the Chinese input and the English translations

• tense concord

• co-referentiality issues for pronouns

Most textual discrepancies between the sizes of the sentences in the two languages were fixed by the fine-tuning as well as hallucinations and Chinese characters in the English translations. We nevertheless noticed a certain number of Chinese characters in the mBART50 translations, which decreased after our fine-tuning, and we only found 18 examples for all the 16,742 sentences, mostly for the fantasy genre, when referring to named entities or specific attributes of the universe (Skills: Blade Technique, Wing Protection, ).

Fine-tuning with Literary Data

In this section, we analyse the outputs qualitatively. This analysis consists of an initial description of the baseline and fine-tuned outputs, followed by a deeper examination of the syntactic and semantic functions of the produced outputs by both models. Instances of hallucinations were observed in the outputs of our baseline model. The hallucinated elements are present in the source text, so they are not elements which are not present in the source text. According to [START_REF] Lee | Hallucinations in neural machine translation[END_REF], hallucinations can be defined as the model producing a vastly different and inadequate output when the source is perturbed under a specific noise model. Thus, we may suggest that there exist other instances where the model ceases translation of the source text and proceeds with generating output punctuated solely by a sequence of continuous commas ("""",), which may represent an alternative manifestation of hallucination. Interestingly, it is noteworthy that the fine-tuned outputs did not exhibit any instances of hallucination. However, it should be mentioned that few Chinese tokens were observed in the fine-tuned outputs. In the Chinese source text, the equivalent of the word "businessmen" is placed at the left periphery of the sentence, having a pragmatic effect that involves topic introduction or re-introduction, based on the context. Both the baseline and fine-tuned models take the left dislocated element to the right periphery of the sentence, thereby inducing an alternation in the sentence's intended meaning. As we observed, the baseline models chunk the sentences and use commas instead of employing coordinations, relative clauses, or more complex structures. In this example, the baseline model produces "Ten minutes later. consciousness is exhausted. scattered" by separating each chunk or even token with a period. In contrast, the fine-tuned model generates "Ten minutes later, his consciousness was exhausted and dissipated.", using coordination to form a united sentence. This represents another instance of the fine-tuned model's proficient manipulation of structures, wherein it employs a relative clause "which" to interconnect the sentences. Ex: "Wang lived in the 413 bedrooms of the West school district, Lins lived in the 413 bedrooms of the East school district." Fine-tuned: 09primary: "Wang Yicheng stayed in 413, which was in the West campus. Lin Sisi stayed in 413, which was in the East campus." Furthermore, the choice of tense seems to be different in the two models: As for the fine-tuned model, a preference for the past tense becomes evident. Conversely, as for the baseline model, an over-use of the present tense is observed in its outputs. We may also add that baseline models tend to favour the indicative mood, which indicates assertion, as seen in an example like "What's wrong with the game?". On the other hand, fine-tuned models have been trained to produce sentences in moods that exhibit a reduced level of assertiveness, as evidenced by constructions like "Could there be a problem with the game?".

BLEU scores

In this section, we report the results of our primary, constrastive1, and contrastive2 in terms of BLEU score computed using Test1 and Test2 datasets at the end of the full training process of each model. The official results of the competition on test3 were not computed as the reference translations were not provided (at the time of writing this article). Table 1 shows that our primary system achieves the same BLEU score as contrastive2 3 , the sentence-level transformer implementation. We notice that the document-level system (contrastive1) is not better than the sentence-level model. This might be explained by the few epochs used for training.

Model

Discussion

Lexical Complexity

To appreciate the relative complexity of the terms used in the translations we first qualitatively compared the translations and contrastive2 seemed to be more elaborate, so we tested this impression with more quantitative means. We investigated the vocabulary growth curves of the three translations using the functions available from the languageR package [START_REF] Baayen | lan-guageR: Analyzing Linguistic Data: A Practical Introduction to Statistics. R package version 1[END_REF] to find out that the number of different types progress on the same rhythm for the different translations. In this type of representation, the horizontal axis corresponds to the expansion of the translation corpus (number of tokens) and the vertical axis corresponds to the number of types. The first lower series of curves corresponds to the number of hapaxes. As can be seen in Figure 1, the progression is very similar for the different translations we produced, while the mBART fine-tuning translation (primary) seems to be more verbose as the translation contains pore tokens than the two contrastive translations. The difference between our different models is clearly not lexical.

Challenging Literary Aspects of the Test Set

The first challenge was the size of the testing data, which resorted to different text genres, but was 30 times bigger than other challenge datasets like for the biomedical task in 2021. An additional difficulty was the paucity of metadata for the 14 genres or for chapter attributions (22 announced and 12 found).

Translation Quality analysis based on

Error Annotation

Quality overview

In total, 109 sample segments were randomly selected from the twelve translated texts generated by the fine-tuned mBART50 model. Based on these sample segments, each translated text was assigned an overall grade individually by three annotators on a scale of 1 to 10, with 1-3 denoting "Very Poor", 4-6 denoting "Poor", 7-8 denoting "Moderate", and 9-10 denoting "Good". The annotators are native Chinese speakers with near native level of English competence. They work in the domain of translation training and linguistics with an advanced proficiency of Chinese-English translation. The three grades given by the annotators for each text were then averaged to obtain a relative ranking of each translation. Overall, the twelve translations achieved an average score of 5 out of 10 in general, with a standard deviation of 0.87. Specifically, seven subgenres were identified among the twelve texts, namely: fantasy (4 texts), ancient romance (2 texts), military (1 text), thriller (1 text), modern romance (2 texts), sci-fi (1 text), and online games (1 text). All the sub-genres are typical in contemporary web novels. Notably, there is not a clear cut between different sub-genres and this categorisation is for analytical purposes only. Among the identified subgenres, the ranking from high to low quality is as follows: thriller (6.0 out of 10), fantasy (5.7), online games (5.4), sci-fi (5.0), ancient romance (4.7), modern romance (4.6), and military (3.8). While subgenre types might be a factor in influencing the quality of the translation given their language styles (e.g., the proportion of conversational segments, terminologies, formality, etc.), this line of discussion requires further evidence. Among the sample segments, the quality and language style of individual source text seem to play a more vital role in the overall quality of the translations. Several prominent error types linking to the stylistic features of the texts were identified, as detailed below.

Error typology

To obtain a more detailed insight into the quality of these translations, the sample segments were annotated based on the error typology introduced by (Hansen and Esperança-Rodier, V.2023). The original typology was further categorized for the Chinese -English language pair and inter-rater validation purposes. Specifically, six level-one error types were identified:

• semantic errors (SEM): errors that directly affect the meaning of the text, involving issues like omission, addition, or wrong translation of content/nuance of content;

• logical, structural and cohesion errors (LSC): errors related to the logical flow and coherence of the text, affecting how different parts relate to each other;

• grammatical errors (GRM): errors related to Figure 1: Vocabulary growth curves of our three translations (primary, contrastive1, contrastive2). The lower series of curves corresponds to the hapaxes for primary, ontrastive1 and contrastive2.

the rules of language such as gender, number, tense, and person etc.;

• stylistic errors (STY): errors regarding the style, tone, and appropriateness of the language used;

• stuttering (STU): words repeated for no apparent reason by the MT system;

• non-translation (NTR): source term left untranslated in the target.

Each level one error type contains specific level two and sometimes level three error types. The complete error typology tailored for this task can be found in the appendix. We use Fleiss' kappa to measure the Level 1 error type inter-rater agreement, and the overall Fleiss' kappa score is 0.288, which can be interpreted as "Moderate agreement" according to (Landis and Koch, 1977)'s classifications. . Fleiss' kappa of Level 1 subgenre annotations is presented in Table 2.

Among all annotated segments, 30.58% segments are considered error-free. 47.71% of them belong to the SEM error type, with the remainder of 11.31% on STY, 4.89% on LSC, and 3.98% on GRM.

Prominent Error Types

Understanding the text in its original language is the basis for literary translation, which requires multi-faceted considerations pinned by context, literary style and cultural nuance. The fine-tuned sys- tem attempts to address the greater-than-sentencelevel textual features. However, human annotation results have shown that it continues to struggle with contextual analysis, which leads to prominent errors such as non-translation, mistranslation and inconsistent translation or reference of proper nouns and terms, mistranslation of idioms, etc.

Transliteration is the main way of addressing the character names from Chinese into English (in this case, standard Pinyin is used). Surprisingly, the system failed to maintain consistency of reference to name entities, for example, "宋扶" (song fu) was translated as "Song Fu", "Song Fudge" and "Song Yidao" at places. The character "宋扶" is also mentioned as "宋师弟" or "宋师兄", which were translated literally (see examples in table 3, handannotated in bold). Given the nature of fantasy (xianxia) novels, "师兄" (senior brother) or "师弟" (junior brother) is a common way of addressing

王 子 法 一 脸 惊 讶 道 :"师 兄 此 话 怎 讲?"
"What do you mean, senior brother?" Prince Charming asked in surprise.

郑 金 龙 笑 眯 眯 道:"师弟,你是在 跟 我 装 糊 涂 吗 ? 宋 师 弟 的 死 , 你 们 不 准 备 给 师 门 一 个 交 代?"
"Junior brother, are you playing dumb?" Zheng Jin Long said with a smile."You don't want to give your sect an account for Junior brother Song's death?"

王 子 法 面 容 一 肃 , 沉 声 道 :"宋 师 兄 差 点 坏 我 蓝 玉 门 好 事 , 宋 扶 该 死 ! 再 给 我 们 一 次 机 会 , 我 们 还 是 会 这 样 做!"

"Senior

Brother Song almost ruined our Lanyu Sect's business. Song Fudge deserves to die! Give us another chance, and we'll still do this!" the prince said solemnly. people under the same sect. Literal translation in this particular context might reduce textual cohesion and such inconsistent reference might confuse target language readers given the numerous consecutive mentions of "brother" in the text. The same issue was observed in the document-level model (contrastive1) result too.

It is difficult for the system to identify a named entity if the name itself or part of the name can be used as a proper noun. For example, "王子法" (wang zi fa) was mistranslated as "Prince Charming", which was because the system misidentified the first two Chinese characters "王子" (wang zi, literal meaning: prince) as a named entity.

Other inconsistency regarding proper nouns lies in the formality of presentation, i.e., case error, meaning translation going against previous choices regarding the capitalization of series-specific terms. In fantasy novels, sect names and martial arts techniques are prominent terms. However, the capitalization of these terms was not always consistent.

It is challenging for the current system to capture ideas or emotions in culturally specific expressions. For example, the idiom "天下没有不散的宴席" is translated as "there is no such thing as a banquet in the world". As a literal translation, it omitted the important part of the idiom "不散的" (literal meaning: non-separable / never-ending), which leads to the failure of conveying its figurative meaning "All good things must come to an end". On the contrary, it did well in translating "哑巴吃黄连" (literal meaning: a mute person eats bitter melons) as "speechless". The discrepancy between the translation quality of idioms shows that more culture-specific training data is needed to improve the accuracy and idiomaticity of literary machine translation.

Sentence-vs. Document-based Training Strategies

An important aspect of the competition was the choice to use full chapters with contextualised successive sentences instead of (more) limited contexts usually retained for translation competitions. This resulted in a much bigger dataset than for more standard competitions (in the vicinity of 400 sentences for biomedical tasks). We submitted 2 models based on a similar architecture: Contrastive1 and Contrastive2.

We used as Contrastive2 a context-agnostic sentence-level transformer model as in [START_REF] Vaswani | Attention is all you need[END_REF] trained on 10 epochs.

We used as Contrastive1 an on-context transformer model with the exact same architecture as Contrastive2 but that adopts sliding windows of 3 concatenated sentences pre-trained on 10 epochs to the sentence-level and trained on 4 epochs with concatenated sentences.

Concatenation of 3to3 implies that the source sentence is concatenated to the two previous sentences using end-of-sentence tokens between each of them. A sliding windows is when sliding-KtoK model encodes the source windows sentences x i K using the end to sentence tokens <eos> and a special token <S> used to mark sentence boundaries in the concatenation then decode the translation y i K x i K = x i-K+1 <S>x i-K+2 <S>...<S>x i <eos> y i K = y i-K+1 <S>y i-K+2 <S>...<S>y i <eos> Another Contrastive model was trained, but unfortunately too late for the submission, based on [START_REF] Lupo | Focused concatenation for context-aware neural machine translation[END_REF]) it has the same specificity than Contrastive1 with a context discount of 0.01. Context-discount means that the loss function is defined as :

L CD (x j K , y j K ) = CD • L context + L current
After the submission period, we continued training our contrastive systems. After 55 epochs of sentence-level pre-training and 14 epochs of document-level training, the system achieved a BLEU score of 21.46 on Test1 test set.

Further Research

Related Research

This subsection discusses related papers.

For fine-tuning mBART, we replicated the parameters tested by [START_REF] Lee | Pretrained multilingual sequence-to-sequence models: A hope for low-resource language translation[END_REF], namely retraining for three epochs. With the same parameter, [START_REF] Namdarzadeh | Fine-tuning mbart-50 with french and farsi data to improve the translation of farsi dislocations into english and french[END_REF] have fine-tuned Persian→English and Persian→French with a single short story but nevertheless observed dramatic improvement for Persian→French translations in terms of elimination of hallucinations, English words and morpho-syntactic correction. We have not tried other multilingual Large Language Models such as mBERT [START_REF] Wu | Beto, bentz, becas: The surprising cross-lingual effectiveness of BERT[END_REF] (based on BERT), mT5 [START_REF] Xue | mt5: A massively multilingual pre-trained text-to-text transformer[END_REF], XLM-R [START_REF] Conneau | Unsupervised cross-lingual representation learning at scale[END_REF] based on RobertA or the more recent (and bigger) Bloom model [START_REF] Teven | Bloom: A 176bparameter open-access multilingual language model[END_REF].

For concatenation Transformer, we used some parameters tested by [START_REF] Lupo | Focused concatenation for context-aware neural machine translation[END_REF]) that translated English→German and English→Russian to observe dramatic improvement on Contrapro set [START_REF] Müller | A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation[END_REF] and English→Russian set [START_REF] Voita | When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion[END_REF] although with only a slight improvement in BLEU score.

Future Research

This first collaboration between several universities and backgrounds has discussed English input and was an opportunity to discuss the findings of the competition on literary data and also our insights into the fine-tuning of mBART50 with literary data. We aim to replicate this analysis on Farsi data, as Farsi is one of the 50 languages of mBART50. As is often the case in competitions, we did not train as much as we expected. For the fine-tuning of mBART, we managed to train for three epochs, which is what we found in previous studies [START_REF] Lee | Pretrained multilingual sequence-to-sequence models: A hope for low-resource language translation[END_REF], but for two other submissions, we were training from scratch and could only manage to train for 10 epochs for constrastive2 (sentencelevel) and fine-tune for 4 epochs for contrastive1 (document-level). This impacted our results. Evaluating our BLEU score on Test1, we got 22.31 BLEU score for both primary and contrastive2 meanwhile 19.03 BLEU score for constrative1.

Conclusion

This paper presented the MAKE-NMTViz system description for the WMT2023 Literary Shared Task. We participated in the Chinese-to-English task with a model trained at sentence level and at document level. We only used the data provided by the organisers but also analysed the translations produced with mBART50 before our submissions. As we did not receive scores from the organisers of the task, we mostly focused on the qualitative analysis of our translations. We resorted to a typology of translation errors and highlighted prominent error types that remained in our translations.

Limitations

During this translation task, we met one limitation with respect to the document-level translation system. In this case, we did not adapt the system to process in Chinese→English language pair. We employed the same setup described in previous works, where the system was trained for English→Russian, English→German and English→French languages.

Table 2 :

 2 Fleiss' kappa of subgenres. κ = 1 is perfect concordance, κ = 0 is no concordance between annotators.

	Subgenre	Score ↑
	Modern Military	0.534
	Science Fiction	0.344
	Ancient Romance 0.321
	Fantasy	0.283
	Modern Romance 0.283
	Thriller	0.152
	Online Game	0.143

Table 3 :

 3 Examples for illustration

Proceedings of the Eighth Conference on Machine Translation (WMT), pages 287-295 December 6-7, 2023. c 2023 Association for Computational Linguistics

Primary and contrastive2 scores on Test1 are identical due to coincidence.

Acknowledgements

This paper emanated from research partly supported by the MAKE-NMTVIZ project, funded under the 2022 Grenoble-Swansea Centre for AI Call for Proposals/ GoSCAI -Grenoble-Swansea Joint Centre in Human Centred AI and Data Systems (MIAI@Grenoble Alpes (ANR-19-P3IA-0003)), and by a 2021 research equipment grant from the Scientific Platforms and Equipment Committee (PAPTAN project) under the ANR grant (ANR-18-IDEX-0001, Financement IdEx Université de Paris). Sadaf Mohseni benefitted from a Collège de France /Université Paris Cité PAUSE scholarship and Nicolas Ballier from a CNRS research leave at LLF (Laboratoire de Linguistique Formelle), which are gratefully acknowledged. This work was also supported by the CREMA project (Coreference REsolution into MAchine translation) funded by the French National Research Agency (ANR), contract number ANR-21-CE23-0021-01.

A Error Typology

• Stuttering.

• Non-translation.


Marc Allassonnière-Tang

Zhong-Liang Gao

Shen-An Chen

Her

Keywords: sortal classifier, Single Origin Hypothesis, Altaic, Mongolic, Tungusic, Turkic

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

Introduction

Numeral classifiers, along with gender/noun class, are the two essential types of nominal classification systems [START_REF] Corbett | Gender[END_REF][START_REF] Aikhenvald | Mechanisms of change in areal diffusion: New morphology and language contact[END_REF][START_REF] Seifart | Nominal classification[END_REF][START_REF] Grinevald | Linguistics of classifiers[END_REF][START_REF] Audring | Gender. Oxford Research Encyclopedia of Linguistics[END_REF], which constitute some of the most common and most important linguistic mechanisms to fulfil the need of the human mind to perceive and organize elements and experiences in a categorized scheme [START_REF] Lakoff | Metaphors We Live by[END_REF]: 162-163, Clahsen 2016: 599). On one hand, an example of the gender/noun class is the masculine/feminine system found in languages such as Spanish, where nouns are assigned to one of the genders found in the language. Such a system has grammatical agreement with other elements of a sentence, e.g., adjectives. On the other hand, examples of numeral classifiers are the Mandarin sortal classifier tiáo for entities with a long shape, as in sān tiáo yú (three clf.long fish) 'three fish' and the mensural classifier bàng 'pound' , as in sān bàng yú (three pound fish) 'three pounds of fish' . In the current study, we only consider numeral sortal classifiers, which are defined as independent morphemes or affixes that categorize nouns according to the inherent features of their referents based on criteria such as shape, consistency and animacy (Allan 1977[START_REF] Grinevald | A morphosyntactic typology of classifiers[END_REF]: 71, Her & Hsieh 2010[START_REF] Kilarski | Classifiers in morphology[END_REF].

In the following text, we thus use the term 'numeral classifier' to refer to 'numeral sortal classifier' . In terms of geographical distribution, the worldwide prevalence of such systems is indicated in two surveys in the World Atlas of Language Structures Online (WALS, Dryer & Haspelmath 2013), i.e., gender/noun class systems: 43.6%, 112/257 [START_REF] Corbett | Number of genders. The World Atlas of Language Structures Online[END_REF]) and classifier systems: 35%, 140/400 [START_REF] Gil | Numeral classifiers[END_REF].

While numeral classifiers have been extensively studied from various perspectives, such as syntax, semantics, discourse, and cognition, the origin of their geographical concentration has yet to be confirmed. One existing hypothesis suggests that numeral classifiers in Asia emerged first in the Sinitic language group and spread to nearby languages, e.g., Tai-Kadai, Austroasiatic, Tibeto-Burman, etc. [START_REF] Her | A single origin of numeral classifiers in Asia and Pacific: A hypothesis[END_REF]. The details of this Single Origin Hypothesis are discussed in Section 2. However, for Altaic classifier languages, only 6 are included and largely left for future research in [START_REF] Her | A single origin of numeral classifiers in Asia and Pacific: A hypothesis[END_REF]. If this hypothesis is on the right track, then classifiers in the Altaic region are unlikely to be indigenous either and are probably a result of diffusion. Based on [START_REF] Hölzl | Sortal numeral classifiers in Central Asia[END_REF] and Chen, Allassonnière-Tang & Her (to appear), this paper further investigates the phylogenetic signals of classifiers in three well-established individual language groups, Mongolic, Tungusic, and Turkic (abbreviated as MTT hereafter). We also consider the phylogenetic tree regrouping these three language groups as if the three share a common root (Proto-MTT). Our purpose is to determine whether classifiers can be justified at Proto-MTT. In Section 3 we present the 55 MTT languages covered in this study. Section 4 then discusses the phylogenetic comparative methods employed and the results of the analyses. In Section 5 we review a qualitative analysis on the classifier inventory of the Mongolic, Tungusic, and Turkic languages to further assess the robustness of the phylogenetic methods. In Section 6 we conclude that the two analyses suggest that classifiers are most likely not present at Proto-MTT.

The single origin hypothesis

As mentioned, an important motivation behind this study was to examine whether classifiers in languages of the Altaic region are consistent with the Single Origin Hypothesis proposed in [START_REF] Her | A single origin of numeral classifiers in Asia and Pacific: A hypothesis[END_REF] based on data of 490 numeral classifier languages worldwide, as shown in Figure 1. Observing the pattern of distribution, where the classifier feature seems to have a clear center of clustering in East and Southeast Asian languages and radiates outward in all directions with classifier languages gradually thinning out and with less intensive use of classifiers [START_REF] Gil | Numeral classifiers[END_REF][START_REF] Her | Defining numeral classifiers and identifying classifier languages of the world[END_REF], [START_REF] Her | A single origin of numeral classifiers in Asia and Pacific: A hypothesis[END_REF] contend that this distribution pattern is by no means accidental and is more likely due to the diffusion of the this feature from the center, which leads to the Single Origin Hypothesis. This hypothesis concerns only classifier languages in Asia and the Pacific and thus excludes classifier languages in the rest of the world, e.g., Europe, Africa, Papua New Guinea, and the Americas. It investigates exactly which language group is the alleged original source of numeral classifiers in Asia and the Pacific. The current results show that Sinitic and Tai are the most likely candidates, and that the available evidence favors Sinitic as the innovator and Tai as a borrower.

According to the Single Origin Hypothesis in its current form, the classifier feature first emerged in Sinitic languages in northern China and all the other classifier languages in Asia and the Pacific acquired this feature via language contact. While fully acknowledging the highly speculative nature of this ambitious hypothesis, the proposers have provided some tentative and circumstantial evidence for a number of language families or groups, including Miao-Yao, Austroasiatic, Tai-Kadai, Tibeto-Burman, Indo-Arya, Dravidian, and Austronesian. However, for languages in the Altaic region (i.e., the MTT languages), they only conjecture that, given a sparse and sporadic number of classifier languages in this region, they have likely acquired classifiers from other language families. No further quantitative analysis has been conducted to investigate the origin of classifiers in MTT languages, which inspires our study to investigate the development of classifiers in MTT languages from a quantitative and qualitative perspective.

On one hand, if quantitative methods show that classifiers are ancestral to MTT languages, then additional analyses are required to further investigate how the time frame matches with the development of classifiers in surrounding language families and how the further findings are consistent with or contradict the Single Origin Hypothesis. On the other hand, if quantitative methods show that classifiers were not present at the root of MTT languages or that classifiers are likely to have been imported in MTT languages, then the findings are consistent with the Single Origin Hypothesis. In both scenarios, the quantitative analysis also needs to be supported by comprehensive surveys that consider classifiers found in individual languages from a linguistic perspective.

Data

Our primary goal is to demonstrate that a classifier system is unlikely to be found at the root of the MTT languages. The root of the MTT family, or the proto-MTT language, though controversial, enjoys the ardent support of a number of researchers, which suggest a common heritage based on lexical, morphological and structural comparisons [START_REF] Robbeets | Diachrony and verb morphology: Japanese and the Transeurasian languages[END_REF][START_REF] Robbeets | The Oxford guide to the Transeurasian languages[END_REF][START_REF] Robbeets | Triangulation supports agricultural spread of the Transeurasian languages[END_REF]. Most notably, in a recent study, [START_REF] Robbeets | Bayesian phylolinguistics reveals the internal structure of the Transeurasian family[END_REF] use lexical etymologies with Bayesian phylogenetic inference to Mongolic, Tungusic, Turkic, Japonic, and Koreanic languages. They find strong support for Tungusic, Mongolic, and Turkic as a unity and Japano-Koreanic as a separate unity. Note that [START_REF] Robbeets | Bayesian phylolinguistics reveals the internal structure of the Transeurasian family[END_REF] call this Mongolic-Tungusic-Turkic group ' A ltaic' . However, to avoid unnecessary confusion, we shall use the more transparent term 'Proto-MTT' to refer to the common ancestral language of the three MTT language groups.

The current study does not include Japonic and Koreanic languages for two reasons. First, [START_REF] Robbeets | Bayesian phylolinguistics reveals the internal structure of the Transeurasian family[END_REF] phylogenetic support for Japon-Koreanic languages as a separate unity is even stronger than that for the MTT group. Second, the existing literature clearly shows that both Japanese and Korean have borrowed substantially from the Chinese numeral system and numeral classifiers; however, indigenous classifiers and an indigenous numeral system also exist alongside the Sino-Japanese and Sino-Korean numeral systems and classifiers. Research on the origin of classifiers in the two languages is rather scarce; there is thus no consensus whatsoever as to whether indigenous classifiers were already in use prior to the loans from their Sinitic neighbors. This issue thus warrants a separate in-depth study and is beyond the scope of the current paper. Nevertheless, we still provide an overview of the classifier systems in Japanese and Korean in Supplementary material 1,1 to allow readers a better understanding of this theoretical choice.

Our data consists of a convenience sample of 55 languages, drawn from WACL (The World Atlas of Classifier Languages) [START_REF] Her | Defining numeral classifiers and identifying classifier languages of the world[END_REF], including 15 Mongolic languages, 9 Tungusic languages, and 31 Turkic languages. This ratio is thought to be an acceptable reflection of the total size of the language families which is 15, 13, and 44 languages [START_REF] Hammarström | Glottolog 4.1. Jena: Max Planck Institute for the Science of Human History[END_REF]. Languages not included in the data had to be removed due to a lack of available data. For each language, grammars, sketches, published chapters, and/or articles were consulted to identify the presence/absence of classifiers. As a soft recall, the term 'classifier' as used in this paper refers to numeral sortal classifiers as mentioned in Section 1. As an example, Kirghiz (Turkic, Glottocode: kirg1245) is considered to be a classifier language since available sources provide examples of classifiers that match our definition, as shown in (1). For example, the classifier tujaq highlights the 'head' feature of a horse and is used in an enumeration context. As an additional note, the sources we consulted also mention that classifiers in Kirghiz are optional. Nevertheless, in our data, we do not differentiate between obligatory and optional classifiers [START_REF] Gil | Numeral classifiers[END_REF]. If a language uses classifiers, whether one or several classifiers and whether obligatory or optional, the language is annotated as having classifiers. As an opposite example, Bonan (Mongolic, Glottocode: bona1250) is annotated as not having classifiers in our data, since a scan of available sources indicated that it does not have sortal numeral classifiers and only has structures similar to mensural classifiers, which are based on containers such as cup and bowl (Fried 2010: 141-142). As shown in (2), the classifier-like structures found in Bonan are not sortal classifiers since they convey an information of quantity and cannot be removed from a clause without affecting its meaning, cf. five noodles vs. five bowls of noodles. The numerals just indicate the number of containers but not the number of nouns, which makes them different from sortal classifiers. A scan for each of the 55 languages resulted in 35 languages without, and 20 languages with, classifiers. Amongst the classifier languages, we found three Mongolic languages (20%, 3/15), three Tungusic languages (33%, 3/9), and 14 (45%, 14/31) Turkic languages. A full list of the included languages and their metadata is available in the supplementary materials. The language name and its affiliated Glottocode are both included to facilitate comparative analyses. Then, the presence/absence of classifiers is annotated, with 1s representing the presence of classifiers and 0s indicating the absence of classifiers. Finally, the phylogenetic affiliation of the language and its geographical coordinates are included (The raw table is included in Supplementary Material 2). A geographical overview of the 55 languages is provided in Figure 2. Each point in the map is extracted from Glottolog and indicates the geographical center point of the geographical area in which the speakers of a language are located. This point may also consider the historical location and/or the demographic center point of language speakers depending on their currently reported number. The geographical distribution of MTT languages with numeral classifiers matches findings reported in previous studies (e.g., Chen, Allassonnière-Tang & Her (to appear)). Classifier languages are not rare in MTT languages, however, they are not the majority, representing one third of the data (36%, 20/55). Most of these classifier languages are located in proximity to Sinitic or Indo-Iranian languages, which are considered to be the two most likely sources of classifier systems in the area.

Phylogenetic analysis

To provide a quantitative diachronic analysis of classifiers in MTT languages, we used phylogenetic comparative methods. These methods originate from evolutionary biology and have been imported by historical linguistics because they can address the non-independence of linguistic features during evolutionary processes (Galton's problem, [START_REF] Mace | A phylogenetic approach to cultural evolution[END_REF]. Such an analysis typically consists of three main steps (Allassonnière-Tang & Dunn 2020). First, the analysis involves the use of a phylogeny, i.e., a family tree of languages. Such a tree or sample of trees are created from historical linguistic knowledge and Bayesian phylogenetic inference. A language family tree can then be used to infer the evolutionary process of a linguistic feature within a language family. Second, the phylogenetic signal of a selected linguistic feature is measured on the tree. A strong signal shows that the trait's evolution is predictable under Brownian motion. A weak signal can point to different scenarios. It could indicate that the diversity of the feature has been affected by non-evolutionary factors such as contact with languages of other families. However, it could also mean that the selected feature is evolving extremely quickly, or that a hidden state probably exists, among others. Third, if the phylogenetic signal is strong, a further analysis can be conducted to infer the evolutionary dynamics of the feature. Additional explanations of this methodology are explained in the following sections. The Bayesian phylogenetic inference was conducted using MrBayes [START_REF] Huelsenbeck | MrBayes: Bayesian inference of phylogeny[END_REF][START_REF] Ronquist | MrBayes 3: Bayesian phylogenetic inference under mixed models[END_REF], and the results assessed with Tracer [START_REF] Rambaut | Posterior summarisation in Bayesian phylogenetics using Tracer 1.7[END_REF], while the phylogenetic comparative analyses are carried out with the Phangorn [START_REF] Schliep | Phangorn: Phylogenetic analysis in R[END_REF], Phytools [START_REF] Revell | Phytools: An R package for phylogenetic comparative biology (and other things)[END_REF], and Ape [START_REF] Popescu | Ape 3.0: New tools for distance based phylogenetics and evolutionary analysis in R[END_REF] packages in R 4.1.2 (R Core Team 2021). The used code is provided in Supplementary Material 3.

Tree inference

Two samples of trees were considered for the phylogenetic analysis. First, we considered the tree sample of trees generated by [START_REF] Robbeets | Bayesian phylolinguistics reveals the internal structure of the Transeurasian family[END_REF]. The trees of the sample were pruned to keep only the languages included both in both the tree sample and our data set. This filter resulted in a smaller sample of 29 languages (7 Mongolic, 6 Tungusic, and 16 Turkic languages). As a way to visualize the overall shape of the tree, the sample of trees can be summarized with a Maximum Clade Credibility tree. To obtain such a tree, we went through the sample of trees and evaluate the likelihood of each branch based on how often a branch occurred. Then, each tree was assigned a credibility score based on the product of the likelihoods of each branch in each tree. The tree with the highest credibility score was the maximum credibility tree and considered to be the representative tree for the entire sample. The maximum clade credibility tree of the sample of trees from [START_REF] Robbeets | Bayesian phylolinguistics reveals the internal structure of the Transeurasian family[END_REF] is shown in Figure 3.

Second, to avoid a coincidental bias resulting from the filter based on the language sample from Robbeets & Bouckaert (2018), we used Bayesian Monte Carlo Markov Chain (MCMC) phylogenetic inference to create a sample of trees to match with the genealogical constraints derived from the Glottolog reference phylogeny and the existing literature (A full list of constraints is provided in the Supplementary Material 4). For the branching structure between the Mongolic, Tungusic, and Turkic language groups, we followed the findings reported by [START_REF] Robbeets | Bayesian phylolinguistics reveals the internal structure of the Transeurasian family[END_REF], who show that the Tungusic group is expected to be an outgroup, followed by a binary branch that consists of the Mongolic and Based on these constraints, we generated a sample of trees in such a way that each tree in the sample represents a historical hypothesis consistent with the established historical linguistic understanding of how these languages are related. The sample as a whole also gives a measure of uncertainty where the Glottolog tree is unresolved (and where existing tree data diverge). As an example, the three Mongolic languages Halh Mongolian, Oirad-Kalmyk, and Peripheral Mongolian follow the same branch in the Glottolog Mongolic tree, but the sub-branch does not specify their hierarchy. That is to say, Glottolog does not specify if Halh Mongolian and Oirad-Kalmyk form a binary branch first, with Peripheral Mongolian as an outgroup, or other combinations. This uncertainty was captured by the sample of the trees, as trees with most possible combinations were included in the sample. Proportional branch lengths were computed using the Grafen method, in the function compute.brlen in the ape package of R (R Core Team 2021).

The set of theoretically possible trees was generated via Markov Chain Monte Carlo (MCMC) methods [START_REF] Metropolis | Equation of state calculations by fast computing machines[END_REF], applying the following principles: First, the sampling starts with an arbitrarily chosen tree. Then, in each gen-eration of the algorithm, this tree is modified and compared with the original tree in terms of likelihood. The MCMC starts from a randomly selected tree with a low likelihood; then, the MCMC changes one small component of the tree at a time and generates trees with a higher likelihood toward the data. This process of generations commonly starts with a burn-in period during which the stationarity is not reached yet. The generations of this period are thus excluded from the dataset. The burn-in phase is typically indicated by the rapid increase in likelihood at the beginning of the MCMC search, and the cut-off point is decided when the likelihood reaches a plateau. Thus, the more generations performed, the more likely it is to simulate evolution under each possible tree in proportion to the posterior probability of the particular scenario. An adequate amount of generations is commonly evaluated via the average standard deviation of split frequencies, which shows the convergence of the sample. In terms of parameters in MrBayes, one run with four chains is conducted. The MCMC run following the constraints from Glottolog resulted in 3000000 generations, among which 1500000 were considered as burn-in and are thus removed. Then, one tree was extracted for each 1500 trees, resulting in a final sample of 1000 trees (mean effective sample size = 1000). The final sample of trees is available in Supplementary Material 5. The maximum credibility tree of the tree sample is shown in Figure 4. The tree conformed to our expectations in two ways. One one hand, languages from the Mongolic, Tungusic, and Turkic groups are not mixed together. Furthermore, the hierarchy of the groups is as we specified when generating the sample of trees: The Tungusic group is an outgroup, followed by the Mongolic and Turkic groups forming a binary branch. On the other hand, the probabilities of the branches match with our settings. As an example, the branching between Even, Evenki, and Oroqen is fixed in Glottolog, i.e., Evenki and Oroqen are first under the same branch, while Even is an outgroup. Each tree in the sample of 1000 trees thus has these two branches, resulting in a probability of 1 (100%) for their occurrence. With regard to the non-constrained nodes, we observe that the uncertainty is rather high, as the posterior probability is low on most of the nodes, being below 0.5. This infers that the probability of ancestral state estimations for the presence/absence of numeral classifiers is bound to be low. Nevertheless, since checking the MCC tree showed that the specified constraints had been applied correctly, we proceeded to measure the phylogenetic signal of classifiers in the sample of trees.

Testing for phylogenetic signal

We first assess the strength of the phylogenetic signal indicating the presence/ absence of classifiers, to provide a statistical measure of how this presence/ absence matches with the phylogeny proposed by historical linguistics and Glottolog. As an example, Figure 4 shows the MTT tree of our study with tip labels colored according to the presence/absence of classifiers in each language. The phylogenetic signal is considered to be strong if the tendency of related languages to resemble each other is stronger than languages drawn at random from the same tree. If languages on the same branch mostly share the same value, it means that the presence/absence of classifiers is easily predictable, which in turn infers a strong phylogenetic signal of classifiers in the tree. However, if languages on the same branch tend to have different values, it means that the presence/absence of classifiers is not easily predictable, which infers a weak phylogenetic signal of classifiers in the tree. A weak signal can be interpreted as the impact of an external influence which has disrupted the inheritance of the trait in the tree.

The measurement of the phylogenetic signal and its testing for statistical significance were conducted with the D statistics [START_REF] Fritz | Selectivity in Mammalian extinction risk and threat types: A new measure of phylogenetic signal strength in binary traits[END_REF]. This measure is typically used to measure the phylogenetic signal of binary traits in a tree, which fits our data and purpose. A D close to 1 indicates that the targeted feature has a distribution that is random compared with the phylogeney. A D close to 0 indicates that the targeted feature matches the Brownian motion model of evolution compared with the phylogeny. In cases where the measured trait is more dis-persed than randomly expected, the D statistics are greater than 1. Vice-versa, if the measured trait is more phylogenetically conserved than as expected under the Brownian motion model of evolution, the D statistics are smaller than 0. Since we have a sample of trees for both trees from [START_REF] Robbeets | Bayesian phylolinguistics reveals the internal structure of the Transeurasian family[END_REF] and the trees generated based on the Glottolog constraints, the D statistic and its statistical significance for classifiers is measured on each tree of the samples. The results are shown in Figure 5. The measured D statistic is close to one for the majority of the trees in both samples. Taking the sample from [START_REF] Robbeets | Bayesian phylolinguistics reveals the internal structure of the Transeurasian family[END_REF] as an example, the mean and the median of the measured D statistic are 0.85 (The first quartile is 0.77 and the third quartile is 0.94). This shows that within all trees of the sample, the phylogenetic signal is generally weak. That is to say, even when considering different trees, the presence/absence of classifiers in MTT languages is not predictable from the phylogeny. This can be interpreted in different ways. One the one hand, a D statistic close to 1 is consistent with a scenario of substantial horizontal transmission. In other words, classifiers are likely to have been borrowed in MTT languages. This distribution of weak signals is significantly different from the expected clumping under a Brownian evolution, and not significantly different from a random association, which confirms that the observed phylogenetic signal is weak in a statistically significant way. On the other hand, borrowing is not the only possible explanation for this result. For example, classifiers could have evolved in a tree-like fashion in MTT languages, just not according to a Brownian motion. Furthermore, a low signal can also be evidence of parallel independent innovation. As an attempt to cover the latter possibilities, we conducted a preliminary analysis inferring the ancestral states of classifiers at the root of MTT languages using both tree samples (included in Supplementary Material 3). The results match with the observed phylogenetic signal. The current tree samples do not provide sufficient information to distinguish if the root of MTT languages had classifiers or not, as the probability of having classifiers at the root of MTT languages is 50%, while the probability of not having them is also 50%.

As a summary, since the phylogenetic signal of classifiers is weak in the MTT languages, we cannot infer the ancestral state nor the transition rates of classifiers in these languages. 3 In our case, two possibilities can be considered: either classifiers were present at the roots of the trees, but were lost in some languages due to contact with non-classifier languages, or classifiers were not present at the roots of the trees, but were acquired in some languages due to contact with classifier languages. As we will show in the following section, the latter hypothesis is more likely.

Qualitative analysis

Since the phylogenetic signal of classifiers was found to be weak in the MTT languages, two main hypotheses need to be considered. On one hand, it is possible that classifiers were present at the root of MTT languages and were lost due to contact. On the other hand, it is also possible that classifiers were not present at the root of MTT languages and were acquired due to contact. We assess the probability of both hypotheses based on the existing literature and a deeper analysis of our data.

A search of the existing literature favors the second hypothesis that classifiers were not present at the respective root of the MTT languages. First, in our research we have not come across any published claims that the classifier feature is indigenous in any of the MTT languages. Furthermore, analyses conducted separately on Mongolic, Tungusic, and Turkic languages show that classifiers in those languages are often borrowed from languages of neighboring languages with classifier systems. For instance, the sortal classifiers found in Tungusic languages such as Manchu are likely to have been collective numeral suffixes, which experienced reinterpretation as classifiers under Mandarin Chinese influence (Alonso de la Fuente 2017: 76). As another example, most of the MTT classifier languages have optional classifiers (Robbeets 2017: 11), as shown with Bonan in (2) and other languages, which is usually the case when classifier systems are not firmly established.

More importantly, a recent qualitative survey by Chen, Allassonnière-Tang & Her (to appear) based on examples from 66 MTT languages identified 14 classifiers languages: two Mongolic languages (Daur and Monguor), three Tungusic languages (Manchu, Evenki, and Xibe), and nine Turkic languages (Azerbaijani, Crimean Tatar, Kazakh, Salar, Tatar, Uyghur, Uzbek, Turkish, West Yugur). A close comparison of etymology and word form has shown that the origin of classifiers in MTT languages has two main possible sources: Persian and Mandarin Chinese.

With regard to classifier languages in Mongolic, taking Monguor as an example, this language has two numeral systems (Slater 2003: 94). The Monguor numeral system has almost been entirely replaced by the Chinese system. Nowadays, only the numbers 1 nige and 2 ghu are still being used. In contexts of enumeration, Monguor numerals are used without numeral classifiers (3a), while borrowed Chinese numerals are used with classifiers (3b). The Monguor classifiers are few and mostly borrowed from Chinese (Slater 2003: 95-96). For example, the most common classifiers are the general classifier ge and the classifier for long objects tiao, which are identical with their Chinese counterparts.

(3) (Slater 2003: 106,184 As an additional evidence to the borrowing of classifiers from Chinese to Monguor, such borrowings are also found in other linguistic domains. For instance, the original word order of Monguor was [Noun Numeral], with the noun preceding the numeral. However, with the borrowing of classifiers, the order [Numeral clf Noun] was also borrowed, which is gradually changing the word order of nouns and numerals in Monguor. Furthermore, the Monguor language has also borrowed a lot of vocabulary from Chinese, including nouns, adjectives, and function words [START_REF] Junastu | Tuzuyu Jianzhi [A Grammar Sketch of Tuzu[END_REF]. Such frequent borrowings show that classifiers in Monguor were most likely borrowed from Chinese.

A similar situation regarding the Chinese influence on classifiers is found in Manchu, Evenki, and Xibe, the three Tungusic classifier languages identified in Chen, Allassonnière-Tang & Her (to appear). Manchu, with a geographical area closest to China and the longest history of interaction with Chinese among MTT languages, has the most classifiers among the MTT languages, as expected. The example in (4a) shows the Manchu classifier giyan for houses and rooms, a clear borrowing of the Chinese classifier jian,. The Xibe example in (5) and the Manchu example in (4b) in turn show the Manchu influence on Xibe.

(4) (Wang 2005: 133, As for classifiers in Turkic languages, taking Turkish as an example, it has five optional classifiers: tane (clf.gen), kişi (clf.human), baş (clf.animal), parça (clf.2d), and aded (clf.gen.formal) [START_REF] Underhill | Turkish Grammar[END_REF][START_REF] Kornfilt | Turkish Descriptive Grammar[END_REF]). On one hand, the optionality and the small inventory size of these classifiers already hint at a borrowed system. On the other hand, these classifiers share similarities with their counterparts in Persian, which also points in the direction of borrowing from Persian. As an example, the most commonly used classifier is the general classifier tane, which has a similar pronunciation and function to the Persian general classifier dane. Other examples of similarities in terms of pronunciation and function can also be found in other classifiers, for example, the classifier adet, used as for tane but only in formal speech, is similar to its Persian counterpart [ʕadad] (van Schaaik 1996). As an additional evidence to the borrowing of classifiers from Persian to Turkish, the two languages are found in a close geographical proximity and vocabulary borrowings from Persian to Turkish are also frequent.

In general, classifiers are not native to any of the Mongolic, Tungusic, and Turkish language groups and are thus not likely to be present at the root of the MTT family. These facts also match with our findings from the phylogenetic analysis, i.e., since classifiers have most likely been introduced in the MTT languages through language contact with either Persian or Chinese, it is expected that the phylogenetic signal of classifiers is rather weak.

We also conducted a closer analysis on languages within our data. Since some languages have already been discussed in Chen, Allassonnière-Tang & Her (to appear), we focus on languages that were not included in the 14 classifiers languages mentioned in that study, i.e., Dongxiang (Mongolic), Kirghiz (Turkic), Qashqa'I (Turkic) and Turkmen (Turkmen). The two other different languages refer to a variation of North and South Uzbek and Azerbaijani, which we do not discuss here. First, Dongxiang is spoken in the western part of China, and is attested to have borrowed classifiers (and also lexical items) from Mandarin Chinese (Field 1997: 240, 247, 264). Second, classifiers in Kirghiz generally have a pronunciation similar to Persian. As an example, in bir daana qalem (one clf.gen pen) 'one pencil' , the general classifier daana is phonologically similar to the Persian general classifier dane. Third, in Qashqa'i, which is spoken by a minority ethnic group in Iran, classifiers are also similar to Persian classifiers [START_REF] Soper | Loan Syntax in Turkic and Iranian: The Verb Systems of Tajik, Uzbek, and Qashqai[END_REF]. For instance, the human classifier tɑ̃n, which literally means 'body' , is phonologically similar to its Persian equivalent ta. Fourth, the general classifier of Turkmen da:ne is also phonologically similar to its Persian counterpart dane. In short, our qualitative analysis of available matches the literature. Classifiers in languages of the Altaic region tend to have been borrowed from neighboring classifier languages, more specifically from Persian or Mandarin Chinese. Furthermore, the effect of contact is also found in non-classifier languages. For example, Bonan is a nonclassifier language. Nevertheless, its numeral phrase word order has been influenced by Amdo Tibetan, which is a classifier language [START_REF] Duojie | Zangyu Anduo fangyan zhong de liangci [The classifiers in Amdo Tibetan[END_REF]. This could suggest that the mensural classifiers found in Bonan could in fact be the starting point of a new classifier system which will eventually develop sortal classifiers.

Conclusion

This study aimed at assessing the likelihood of classifier systems at the root of the MTT family being a unity, our hypothesis being that classifiers in MTT languages are all from a borrowing origin. To accomplish this, we conducted two analyses. First, we used Bayesian phylogenetic inference to measure the phylogenetic signal of classifiers in MTT languages. Our results show that this phylogenetic signal is weak in MTT languages. This indicates that two main possibilities are likely: either classifiers were present at the root of MTT languages and were lost due to contact or classifiers were not present at the root of MTT languages and were acquired due to contact. Then, we provide a qualitative analysis based on a review of the literature to assess which of the two options is the most likely. Our results show that since classifiers found in MTT languages can mostly be traced to an origin of borrowing from either Chinese or Persian, the probability of classifiers at the root of MTT languages is low. These results are thus compatible with the find-ings of [START_REF] Hölzl | Sortal numeral classifiers in Central Asia[END_REF] and Chen, Allassonnière-Tang & Her (to appear) and consistent with the Single Origin Hypothesis.

In terms of further research, regarding the method, the current paper used a binary coding of classifier systems in terms of presence/absence. A more finegrained analysis could be expected to shed more light on the development of classifier systems in MTT languages. For example, future studies could consider the contrasts of obligatoriness/optionality and sortal/mensural nature of classifiers in their quantitative models. Ideally, the phylogenetic signal could also be measured for each classifier individually to evaluate if it is stronger for some classifiers than others. If that is indeed the case, phylogenetic methods of correlated evolution could also be used to infer in which order classifiers developed in MTT languages. Furthermore, as Japonic and Koreanic languages were not included in the current paper, separate in-depth qualitative analyses could be conducted for these languages. Additional research is also needed to investigate the origin of classifiers in Persian. The same analysis could also be replicated on language groups and families found in neighboring geographical areas. For example, classifiers are also found in Austronesian languages. It would thus be relevant to assess the phylogenetic signal and the probability at the root of classifiers in these languages.

Figure 1 .

 1 Figure 1. 490 classifier languages in the world (Her & Li 2023: 22) (Map from WLMS 16 www.gmi.org/wlms. ©2016 Google -Map data ©2016)

  2

Figure 2 .

 2 Figure 2. A geographical overview of the 55 languages included in the data. Neighboring Indo-Iranian languages (green) and Sinitic languages (blue) are shown in shaded colors

Figure 3 .

 3 Figure 3. The maximum clade credibility tree extracted from the sample of trees from Robbeets & Bouckaert (2018). The numbers on the nodes of the tree indicate the posterior probabilities

Figure 4 .

 4 Figure 4. The maximum clade credibility tree extracted from the sample of 1000 trees generated based on Glottolog constraints. The numbers on the nodes of the tree indicate the posterior probabilities (a probability of 1 means a Glottolog constraint)

Figure 5 .

 5 Figure 5. The distribution of the D statistic and its departure from random association (P_Diff_Random) and the clumping expected under a Brownian evolution threshold model (P_Diff_Brownian) based on the two samples of trees from the Mongolic, Tungusic, and Turkic languages. The dashed lines indicate the significance level of .05 for p values

Marc Allassonnière-Tang [唐威洋] et al. © 2023. Department of English, National Taiwan Normal University All rights reserved

© 2023. Department of English, National Taiwan Normal University All rights reserved

Marc Allassonnière-Tang [唐威洋] et al. © 2023. Department of English, National Taiwan Normal University All rights reserved

The link to the supplementary materials is provided at the end of the paper in the Data Availability Statements section, before the References.Origin of sortal classifiers in Altaic languages 299 © 2023. Department of English, National Taiwan Normal University All rights reserved

This definition of classifier languages is widely adopted in the literature, e.g.,[START_REF] Gil | Numeral classifiers[END_REF] and[START_REF] Her | Defining numeral classifiers and identifying classifier languages of the world[END_REF], and is thus without controversy. Furthermore, for the purpose of the current paper, i.e., to identify the origin of sortal classifiers in MTT languages, it is more important to identify sortal classifiers in these languages than to determine what qualifies a language to be a real classifier language. 300 Marc Allassonnière-Tang [唐威洋] et al. © 2023. Department of English, National Taiwan Normal University All rights reserved

Marc Allassonnière-Tang [唐威洋] et al. © 2023. Department of English, National Taiwan Normal University All rights reserved

Marc Allassonnière-Tang [唐威洋] et al. © 2023. Department of English, National Taiwan Normal University All rights reserved

We also considered conducting phylogenetic analyses on Mongolic, Tungusic, and Turkic languages separately. However, the small sample size of the Mongolic and Tungusic languages would make the results less robust for such an analysis.Origin of sortal classifiers in Altaic languages 307 © 2023. Department of English, National Taiwan Normal University All rights reserved

Marc Allassonnière-Tang [唐威洋] et al. © 2023. Department of English, National Taiwan Normal University All rights reserved

Acknowledgements

The authors are thankful for the support of Martine Robbeets. The authors would like to thank the anonymous reviewers and also the editors for their constructive comments and suggestions, which helped improve the paper. Any errors that may remain are our sole responsibility.

Funding

Marc Allassonnière-Tang would like to thank the support of the French National Research Agency (ANR-20-CE27-0021). O.-S. Her, the corresponding author, gratefully acknowledges the financial support of the following grants by Taiwan's National Science and Technology Council (NSTC): 106-2410-H-029-077-MY3, 108-2410-H-029-062-MY3, 111-2410-H-029-009-MY3.

Data availability statements

Supplementary materials including the data and code underlying this article are available in OSF (Open Science Framework) at https://osf.io/u6wfg/?view_only=None, and can be accessed directly.

List of abbreviations