A modal sense classifier for the French modal verb pouvoir
Authors: Anna Colli, Diego Rossini, Delphine Battistelli (Paris Nanterre University, 2024)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper we address the problem of modal sense classification for the French modal verb pouvoir in a transcribed spoken corpus. To the best of our knowledge, no studies have focused on this task in French. We fine-tuned various BERT-based models for French in order to determine which one performed best. It was found that the Flaubert-base-cased model was the most effective (F1-score of 0.94) and that the most frequent categories in our corpus were material possibility and ability, which are both part of the more global alethic category.

Introduction
In this paper, we present our research into the automatic disambiguation of the French modal verb pouvoir (in English, this verb can be translated by can, could, may o might) in a corpus of semi-structured interviews1 . This problem statement is part of a broader quantitative and qualitative analysis currently underway on modal markers in order to better understand which kinds of modal categories are prevalent in this kind of corpus. As an NLP task, the problem of the automatic disambiguation of modal markers relies on what is generally called “modal sense classification” (MSC). As far as we know, no studies have focused on disambiguating modal verbs using a machine learning approach in French. Our aim is to fill this gap by finding the best fine-tuned BERT model to classify the semantic values of the French modal verb pouvoir in a transcribed spoken corpus. The article is organized as follows. In section 2 we review related work on the task of modal sense classification. Section 3 describes our corpus and our linguistic model. Section 4 presents the annotation of the corpus with an annotation scheme. Section 5 presents our experiments in fine-tuning different BERT models in order to choose the most effective one. Finally, in section 6 we discuss our results and in section 7 we close our contribution with conclusions and suggestions for future research.

Conclusion
This study demonstrates significant first progress in the automatic classification of the French verb pouvoir by finding the best fine-tuned BERT model. Moderate to substantial inter-annotator agreement led to merging some subcategories for more streamlined annotations. The flaubert-base-cased model, with contextual data augmentation, achieved an impressive F1-score of 0.94 with cross-validation, highlighting the importance of context (see section 4.2 “Corpus Context”). However, challenges persist, such as limited training data and the need for better annotation tools and more powerful computational resources. The model struggles with certain deontic usages that humans easily identify. Intentional ambiguity by the speaker also poses a challenge for both annotators and the model. Future work should expand and enrich the dataset and consider training on full texts instead of isolated sentences to capture context better. [8] propose a similar approach, emphasizing the importance of taking a large context around the target token and advocating for the use of full texts as context. In the future, we will also experiment with an augmented context window of 10 lines before and after the target token. These enhancements will improve model robustness and set the stage for further advancements in natural language processing, particularly for classifying semantic values of French modal verbs. This is the first step in a larger project that will soon include the verb devoir (must). More globally, the ultimate goal of our approach is to be able to identify which modal categories are prevalent in any given corpus [16]. Indeed, given that the verb pouvoir is present in all types of texts, the ability to identify its modality becomes a necessary tool for refining the overall analysis of modality in different tasks such as sentiment analysis ([17] or hedge detection ([18]).