Segmenting a Large F rench Meeting Corpus into Elementary Discourse Units (2025)

Abstract: Despite growing interest in discourse-related tasks, the limited quantity and diversity of discourse-annotated data remain a major issue. Existing resources are largely based on written corpora, while spoken conversational genres are underrepresented. Although discourse segmentation into elementary discourse units (EDUs) is considered to be nearly solved for canonical written texts, conversational spontaneous speech transcripts present different challenges. In this paper, we introduce a large French corpus of segmented meeting dialogues, including 20 hours of manually transcribed and discourse-annotated conversations, and 80 hours of automatically transcribed and discourse-segmented data. We describe our annotation campaign, discuss inter-annotator agreement and segmentation guidelines, and present results from fine-tuning a model for EDU segmentation on this resource.

## introduction discourse processing is gaining increasing attention as performance levels have reached a threshold of practical usability. however, the field still faces significant challenges, due to the limited quantity and diversity of discourse-annotated data. most existing discourse resources are based on written corpora, while spoken conversational genres remain underrepresented. moreover, the largest available resources for spoken discourse are typically limited to english telephone conversations between two participants, despite the broader diversity of real-world conversational settings. in this paper, we introduce a new, sizable french corpus composed of meetings involving 3 to 4 participants. the basic units of discourse processing, known as elementary discourse units (edus), are often approximated using punctuation-based sentence boundaries in written texts, or pauses and speaker changes in spoken dialogue. however, such proxies are imperfect, and discourse segmentation is widely recognized as a necessary step for accurate discourse analysis. while state-of-the-art results suggest that discourse segmentation is nearly solved for written texts-thanks to the presence of reliable punctuation-this is not the case for spontaneous speech. to address this gap, we present the creation of a large, discourse-segmented corpus of french meetings, comprising approximately 20 hours of manually transcribed and segmented conversations, along with 80 hours of automatically transcribed and segmented data. the paper begins with a survey of existing discourse corpora (section 2) and related work on discourse segmentation (section 3), before introducing the french meeting corpus (section 4). we then describe the discourse segmentation campaign (section 5), followed by a proposal for a baseline model, fine-tuned on our annotations and applied to the full dataset (section 6). ## discourse segmented conversational corpora the ami corpus @xcite and the icsi corpus @xcite are among the most well-known resources for meeting conversations. ami includes 137 scenariodriven meetings, each lasting between 15 and 45 minutes, totaling approximately 65 hours of conversation. in each meeting, four participants assume specific roles and interact over a sequence of four sessions. while the language is spontaneous, the scripted design results in conversational styles that are arguably cleaner and more structured than those found in real-life meetings, leading to greater homogeneity in both content and vocabulary. the icsi corpus consists of natural meetings, each about one hour long, totaling roughly 72 hours of recordings. these meetings typically involve six participants discussing technical topics related to natural language processing. since icsi features real meetings between collaborators actively working on projects at the time of recording, the interactions are more naturalistic than those in ami. however, the conversations include a high concentration of technical vocabulary, specialized subject matter, and significant shared knowledge. these factors may complicate interpretation for models lacking this contextual background. the elitr corpus @xcite ) contains transcripts of 113 technical project meetings in english and 53 in czech, totaling over 160 hours of content. the transcriptions include turnlevel segmentation, with turns further punctuated using standard written punctuation marks. additionally, several smaller conversational corpora are available in french (for a recent list, see @xcite . one such example is cid (corpus of interactional data; @xcite , which includes eight one-hour dialogues between friends, annotated with elementary discourse segmentation @xcite . however, with only eight hours of recording, this corpus remains relatively small and is not specifically focused on meetings. ## discourse segmentation discourse segmentation involves breaking down written texts or speech transcripts into units that reflect the communicative intentions of the participants. these units aim to provide a more accurate representation of discourse structure than traditional notions such as 'sentences' or 'speech activity chunks' (e.g., interpausal units). we use the term elementary discourse unit (edu) from @xcite to refer to minimal communicative units, each corresponding to clause-level content that conveys a single fact or event. for a long time, segmentation into edus remained a challenging task, requiring significant human annotation effort to build discourse-segmented corpora. the development of llm-based approaches has opened in a new era, in which these tasks can now be tackled with high accuracy, particularly for written genres and highly resourced languages. large language models (llms) have achieved strong results in discourse segmentation @xcite @xcite . the work carried out within these shared tasks has equipped the community with robust tools and frameworks for edu segmentation. however, as noted by @xcite , performance tends to decline for languages other than english, even in written genres, particularly when gold-standard sentence boundaries are unavailable, due to the imperfections of automatic sentence segmenters @xcite . while recent work such as minixhofer et al. (2023) and frohmann et al. (2024) ## the corpus we provide elementary discourse segmentation for the summ-re corpus @xcite . 1 this corpus consists of approximately 100 sessions made of 3 x 20' meeting, with 2 to 4 participants, focused on event planning. each group takes part in three sessions, each associated with a different task: (i) discussing ideas for the event (more presentation-and monologue-oriented), (ii) deciding what to do at the event (dialogic discussions involving divergent opinions), and (iii) planning the practical organization of the event (including task delegation). most sessions were recorded faceto-face using head-mounted microphones, with a few additional sessions conducted via zoom. the entire summ-re corpus has been automatically transcribed @xcite . the full dev and test sets-corresponding to 73 meetings and approximately 24 hours of recordings-have been manually corrected and annotated. while this may not be considered a large dataset, to the best of our knowledge, it is the largest annotated corpus available for french and among the largest across all languages. we computed some basic figures of the manually annotated part of the corpus, the dialogue hosted in average 534 edus and mean length of an edus is 8 ± 6 (exhibiting a huge variation due to the diversity of the speech acts conveyed by these discourse units). see figures 3 and 4 in the appendix for details. ## discourse segmentation campaign four naive annotators foot_1 were recruited to manually segment the dev and test portions of the corpus. the process began with a discussion of segmentation guidelines-adapted from earlier work on written french @xcite and previously applied to similar datasets @xcite -followed by training sessions during which all debatable decisions made by the annotators were discussed and potentially revised. we held three of such sessions, using both an external corpus @xcite and a few meetings from our own dataset. after each session, annotators conducted a short segmentation exercise, and interannotator agreement was evaluated. once convergence was sufficient, as measured by the interannotator κ score, we proceeded with the full annotation task. while discourse segmentation involves many straightforward decisions, some cases can be quite challenging-particularly in the context of spontaneous conversational speech. the segmentation was then performed using praat @xcite to avoid requiring specialized tool training and to ensure precise timing of each participant's contributions. annotation was conducted speaker by speaker, with the other participants' contributions used as contextual information. thus, for a conversation involving four participants, a separate segmentation file was created for each speaker, and the session was analyzed four times. ## automatic discourse segmentation the primary focus of our experiments is on finetuning large language models (llms), specifically xlm-roberta-large @xcite . our models follow a sequence-to-sequence architecture, fine-tuned using the methodology proposed by @xcite within the jiant framework @xcite . 3 the task is defined within the disrpt framework. data is encoded in the conll format, with a simple binary label indicating whether a token marks the beginning of a discourse unit (see we used xlm-roberta @xcite with a learning rate of 10 -5 , a batch size of 1, gradient accumulation of 4, and a maximum of 30 epochs with a patience of 10, based on performance on the development set. results by training set size are reported in ## conclusion in this paper, we introduced a manual segmentation of a large meeting corpus and presented a straightforward fine-tuning approach to build a segmentation model. the model, along with its application to the full dataset, will be released alongside the code repository. our findings indicate that fine-tuning for this task is highly effective, requiring relatively mod-est amounts of annotated data to achieve strong performance. notably, the model's performance plateaued after a certain point, aligning with the inter-coder agreement levels observed in our study. nevertheless, the extensive gold-standard annotations we produced remain valuable for future studies. from a linguistic perspective, this substantial (20-hour) manually discourse-segmented corpus offers a rich resource for exploring the discourse-prosody interface in multiparty conversations. for future work, we are interested in assessing the extent to which discourse segmentation can be effectively applied to the asr versions of the corpus. while an f-score of approximately 0.86 makes the method more promising than typical proxies, it still falls short of the scores typically achieved on written documents for this task. one potential avenue we would like to pursue is collecting more segmentations from expert (rather than naive) annotators. given the efficiency of finetuning observed in our study, fine-tuning on a small amount of expert annotations or using them in a many-shot approach @xcite ) could be a promising strategy.