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# Segmenting a French Meeting Corpus into Elementary Discourse Units

Laurent Prevot, Julie Hunter, Roxane Bertrand

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Laurent Prevot, Julie Hunter, Roxane Bertrand. Segmenting a French Meeting Corpus into Elementary Discourse Units. Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Aug 2025, Avignon, France. hal-05312254

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# Segmenting a French Meeting Corpus into Elementary Discourse Units

Laurent Prévot

Aix Marseille Univ. & CNRS, LPL

CNRS & MEAE, CEFC

laurent.prevot@univ-amu.fr

Roxane Bertrand

CNRS & Aix Marseille Univ., LPL

Aix-en-Provence, France

roxane.bertrand@univ-amu.fr

Julie Hunter

LINAGORA Labs

Toulouse, France

jhunter@linagora.com

## 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 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.

## 1 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).

## 2 Discourse Segmented Conversational Corpora

The AMI corpus (Carletta et al., 2005) and the ICSI corpus (Janin et al., 2003; McCowan et al., 2005) are among the most well-known resources for meeting conversations. AMI includes 137 scenario-driven 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 work-
ing on projects at the time of recording, the inter-
actions are more naturalistic than those in AMI.
However, the conversations include a high concen-
tration of technical vocabulary, specialized subject
matter, and significant shared knowledge. These
factors may complicate interpretation for models
lacking this contextual background.

The ELITR corpus (Nedoluzhko et al., 2022) contains transcripts of 113 technical project meetings in English and 53 in Czech, totaling over 160 hours of content. The transcriptions include turn-level segmentation, with turns further punctuated using standard written punctuation marks.

Additionally, several smaller conversational corpora are available in French (for a recent list, see Hunter et al., 2023). One such example is CID (Corpus of Interactional Data; Blache et al., 2017), which includes eight one-hour dialogues between friends, annotated with elementary discourse segmentation (Prévot et al., 2021). However, with only eight hours of recording, this corpus remains relatively small and is not specifically focused on meetings.

## 3 Discourse Segmentation

Discourse segmentation involves breaking down
written texts or speech transcripts into units that
reflect the communicative intentions of the par-
ticipants. These units aim to provide a more ac-
curate 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 Asher
and Lascarides (2003) to refer to minimal commu-
nicative units, each corresponding to clause-level
content that conveys a single fact or event.

For a long time, segmentation into EDUs re-
mained a challenging task, requiring significant hu-
man annotation effort to build discourse-segmented
corpora. The development of LLM-based ap-
proaches has opened in a new era, in which these
tasks can now be tackled with high accuracy, par-
ticularly for written genres and highly resourced
languages.

Large Language Models (LLMs) have achieved
strong results in discourse segmentation (Zeldes

et al., 2019, 2021; Braud et al., 2023). The work
carried out within these shared tasks has equipped
the community with robust tools and frameworks
for EDU segmentation. However, as noted by
Braud et al. (2023), performance tends to decline
for languages other than English, even in written
genres, particularly when gold-standard sentence
boundaries are unavailable, due to the imperfec-
tions of automatic sentence segmenters (Braud
et al., 2017). While recent work such as Minix-
hofer et al. (2023) and Frohmann et al. (2024) is
less reliant on punctuation, it remains anchored in
textual domains.

A recent trend in discourse segmentation leverages sequential models over contextual embeddings (Wang et al., 2018; Muller et al., 2019). Specifically for spontaneous speech, Gravellier et al. (2021) proposed a weak supervision approach (Ratner et al., 2017), where manually crafted heuristic rules, including some based on a model trained on written data (Muller et al., 2019), were used to annotate the corpus. This noisy supervision was then used to fine-tune BERT (Devlin et al., 2019). In Prevot et al. (2023), a larger set of manual annotations enabled a direct comparison between fine-tuning with a sizable training dataset and weak supervision. Metheniti et al. (2023a) introduced improvements over Muller et al. (2019), achieving new state-of-the-art results across multiple languages. Building on this line of work, Prevot and Wang (2024); Prevot et al. (2025) demonstrated that fine-tuning ROBERTA provides a promising general approach to discourse segmentation, even for lower-resource languages.

Working with conversational speech transcripts,
rather than written text, introduces several chal-
lenges. First, there is no ground-truth punctuation
in such data; instead, it is replaced by pauses and,
in multimodal setups, prosody (which is not the
case in this paper, but see (Gravellier et al., 2021;
Prevot and Wang, 2024) for similar experiments in-
volving prosody). The specific flow of spontaneous
speech introduces another difficulty, as disfluencies
are frequent and often disrupt canonical syntactic
structures.

Moreover, the conversational nature of the data
introduces two additional challenges. First, turn
alternation can significantly disrupt the sequences.
Second, discourse units exhibit much greater vari-
ability in content compared to written documents.
More precisely, they include explanatory, narrative,
and other monologic sequences, as well as shorter
interactional units corresponding to conversational
feedback and short answers.

Regarding major dialogic phenomena, whenever
a *backchannel* corresponds to a full dialogue act, it
is segmented accordingly. It is important to note
that segmentation is performed independently for
each participant, although annotators are allowed
to see the full multologue context.

In the case of *hesitations*, we distinguish be-
tween completely abandoned false starts—which
cannot be semantically or syntactically related to
what follows (e.g., due to an inconsistent sub-
ject)—and hesitations that are disfluent yet coher-
ent preparations for the upcoming content. In the
former case, the abandoned units are segmented
as separate discourse units; in the latter, they are
grouped with the following segment.

Although laughter is a crucial phenomenon in
dialogue (Ginzburg et al., 2020), it was not consid-
ered in our segmentation work.

4 The Corpus

We provide elementary discourse segmentation
for the SUMM-RE corpus (Hunter et al., 2024).<fnref n="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 differ-
ent task: (i) discussing ideas for the event (more
presentation- and monologue-oriented), (ii) decid-
ing 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 face-
to-face using head-mounted microphones, with a
few additional sessions conducted via Zoom.

The entire SUMM-RE corpus has been automat-
ically transcribed (Yamasaki et al., 2023). 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 dia-
logue 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.

5 Discourse Segmentation Campaign

Four naive annotators<fnref n="2" /> were recruited to manu-
ally segment the dev and test portions of the cor-
pus. The process began with a discussion of seg-
mentation guidelines—adapted from earlier work
on written French (Muller et al., 2012) and pre-
viously applied to similar datasets (Prévot et al.,
2021)—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
(Blache et al., 2009) and a few meetings from our
own dataset. After each session, annotators con-
ducted a short segmentation exercise, and inter-
annotator agreement was evaluated. Once con-
vergence was sufficient, as measured by the inter-
annotator κ score, we proceeded with the full anno-
tation 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 (Boersma, 2002) to avoid requiring special-
ized tool training and to ensure precise timing of
each participant's contributions. Annotation was
conducted speaker by speaker, with the other par-
ticipants' contributions used as contextual infor-
mation. Thus, for a conversation involving four
participants, a separate segmentation file was cre-
ated for each speaker, and the session was analyzed
four times.

5.1 A Two-Step Approach

The annotation process was carried out in two steps.
First, a small subset of the corpus was segmented
manually by the naive coders. This subset was
then used to train an initial rough segmentation
model. We applied this model to the entire corpus
to produce pre-segmented files, which the coders
subsequently edited. As previously mentioned, seg-
mentation involves a majority of easy cases that the
model was able to learn quickly. The main bene-
fit of this approach was a significant reduction in
annotation time: Annotators only needed to edit
an existing Praat file rather than create all the time
boundaries from scratch—a notably tedious task.

<footnote n="1">¹https://huggingface.co/datasets/linagora/
SUMM-RE</footnote>

<footnote n="2">²Undergraduate students from the School of Humanities.</footnote>
Even a rough baseline pre-segmentation proved extremely helpful in this context.

Such a two-step method raises the concern that the resulting dataset might be overly biased by the pre-segmentation. To address this, we conducted two checks. First, we manually compared the pre-segmentation and the final manual segmentation on a sample of sessions and found that the annotators made effective use of the pre-segmentation. For instance, spontaneous dialogue often includes short feedback utterances, which are preceded and followed by long speaker pauses; while such segments require multiple operations to annotate from scratch, they were consistently handled correctly in the pre-segmentation.

Second, we performed inter-coder agreement analysis on both files annotated from scratch and those annotated with pre-segmentation, and observed no significant differences between the two. This does not mean that the dataset is entirely free of bias introduced by pre-segmentation, but if any bias exists, it appears to be smaller than the variability introduced by inter-coder differences, as shown in Figure 1. Overall, semi-naive coder reached mean κ-scores within the 0.85-0.89 range, while a few expert annotations conducted by the authors of the paper have shown higher reliability, with κ-scores around 0.9.

<figure><img src="image_3.png" /><figcaption>Figure 1: F-scores between human coders. Red dots correspond to agreement on files that were not pre-segmented.</figcaption></figure>

## 5.2 Segmentation Guidelines

We adopted a discourse approach that is designed to be simple enough for semi-naive coders to apply after some training. While we acknowledge the multidimensional nature of discourse phenomena, our approach focuses on a single layer of discourse

units. Although multiple overlapping layers are analytically justified (Petukhova et al., 2011; Hu and Degand, 2023), we believe that both conversation participants and semi-naive coders can intuitively identify discourse units as conversational actions, without explicitly attending to these complex overlapping layers. To summarize, our definition of EDU is proposed below, in (1).

Our guidelines use both holistic interpretation of discourse function within the discourse flow as understood by the annotator and surface cues such as discourse markers. This operationalization combines both top-down and bottom-up perspectives. This reflects the intermediate position of EDUs, which function as the largest units within the domain of grammar and language, and the smallest units within discourse structure. The complete segmentation guidelines are provided as Supplementary Material.

(1) **Elementary Discourse Unit (EDU)** In a speech transcript, an EDU is defined as a span of contiguous tokens whose combined meaning corresponds to a semantic proposition (describing an event, fact, or state of affairs), or a single speech act, such as asking a question, providing an answer, or offering conversational feedback. All preparatory disfluent material should be included within the unit.

A unit is only labeled as an *abandoned discourse unit* if the introduced material is discarded before the beginning of a different discourse unit.

## 6 Automatic Discourse Segmentation

The primary focus of our experiments is on fine-tuning large language models (LLMs), specifically xlm-roberta-large (Conneau et al., 2019). Our models follow a sequence-to-sequence architecture, fine-tuned using the methodology proposed by Metheniti et al. (2023b) within the JIANT framework (Pruksachatkun et al., 2020).<fnref n="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 Table 2). Evaluation is based on F-score, precision, and recall, computed exclusively for discourse boundaries (true negatives are excluded from the

<footnote n="3">³See https://github.com/phimit/jiant-discut</footnote>
score).

We used XLM-ROBERTA (Conneau et al., 2019) 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 Table 2.

<figure><img src="image_4.png" /><figcaption>Figure 2: F-scores according to training data size. In red, the mean value for human annotations.</figcaption></figure>

The results in Figure 2 indicate that the model's performance plateaus relatively quickly. Once the naive coders were trained, we estimated that segmenting 1,000 tokens into EDUs would take approximately half a day per person, 10,000 tokens about a week, 50,000 tokens around a month, and 100,000 tokens roughly two months. Notably, this performance plateau seems to correspond to the maximal theoretical level achievable, as it aligns with the inter-coder agreement levels observed in our study. It appears that we performed more manual segmentation than was strictly necessary to achieve optimal model performance. Nevertheless, this extensive gold-standard dataset will be valuable for future research and could contribute to the development of more robust discourse segmentation models.

# 7 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 fine-tuning observed in our study, fine-tuning on a small amount of expert annotations or using them in a many-shot approach (Agarwal et al., 2024) could be a promising strategy.

# Acknowledgments

We would like to thank our discourse annototators: Eliane Bailly, Océane Granier, Manon Méaume and Lyne Rahabi. We gratefully acknowledge support from the ANR-funded project, SUMM-RE (ANR-20-CE23-0017).

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# A Appendix

## (2) Eventualities

a. [on y va avec des copains]$_{du}$ [on avait pris le ferry en Normandie]$_{du}$
[we are going there with friends]$_{du}$ [we took the ferry in Normandy]$_{du}$
[puisque j'avais un frère qui était en Normandie]$_{du}$ [on traverse]$_{du}$
[since I had a brother that was in Normandy]$_{du}$ [we cross]$_{du}$
[on avait passé une nuit épouvantable sur le ferry]$_{du}$
[we spent a terrible night on the ferry]$_{du}$

b. [j'ai eu plusieurs conflits avec des animateurs pas assez sérieux]$_{du}$
[I had several conflicts with group leaders that were not serious enough]$_{du}$

c. [et y en a un qui s'était pris un banc de pierre]$_{du}$
[and there was one that hit a stone bench]$_{du}$

## (3) Speech Act / Clear Communicative Function

a. A: [Tu vois où c'est?]$_{du}$ B: [oui]$_{du}$

A: [You know where it is?]$_{du}$ B: [Yes]$_{du}$

b. A: [Je ne voulais pas les déranger]$_{du}$ B: [oui bien sûr]$_{du}$

A: [I did not want to disturb them]$_{du}$ B: [Yes of course]$_{du}$

## (4) Discourse Markers inducing segmentation

a. [on a appelé euh des les parents d'amis]$_{du}$ [mais pas d'amis de notre âge d'amis de mes parents]$_{du}$
[we called um some friend's parents]$_{du}$ [but not friends of our age friends or my parents]$_{du}$

b. [donc on était à Montréal en fait]$_{du}$ [et après le congrès on est parti en Gaspésie]$_{du}$
[so we were in Montreal in fact]$_{du}$ [and after the conference we left to Gaspésie]$_{du}$

<table><caption>Table 1: Results for Discourse Segmentation for different amount of training gold data.</caption><thead><tr><td>nb train tokens</td><td>prec</td><td>recall</td><td>fscore</td></tr></thead><tbody><tr><td>1000</td><td>0.869</td><td>0.706</td><td>0.779</td></tr><tr><td>10000</td><td>0.880</td><td>0.826</td><td>0.851</td></tr><tr><td>50000</td><td>0.876</td><td>0.850</td><td>0.863</td></tr><tr><td>100000</td><td>0.900</td><td>0.831</td><td>0.864</td></tr></tbody></table>

<table><caption>Table 2: Illustration of the data format for the discourse segmentation task.</caption><thead><tr><td>id</td><td>token</td><td>connll</td><td>Discourse Boundary</td></tr></thead><tbody><tr><td>1</td><td>ouais</td><td>-------</td><td>BeginSeg=Yes</td></tr><tr><td>2</td><td>#</td><td>-------</td><td>-</td></tr><tr><td>3</td><td>on</td><td>-------</td><td>BeginSeg=Yes</td></tr><tr><td>4</td><td>dirait</td><td>-------</td><td>-</td></tr><tr><td>5</td><td>des</td><td>-------</td><td>-</td></tr><tr><td>6</td><td>enfants</td><td>-------</td><td>-</td></tr><tr><td>7</td><td>#</td><td>-------</td><td>-</td></tr><tr><td>8</td><td>hein</td><td>-------</td><td>-</td></tr><tr><td>9</td><td>#</td><td>-------</td><td>-</td></tr><tr><td>10</td><td>mais</td><td>-------</td><td>BeginSeg=Yes</td></tr><tr><td>11</td><td>les</td><td>-------</td><td>-</td></tr><tr><td>12</td><td>enfants</td><td>-------</td><td>-</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>Figure 3: EDU counts per dialogue</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 4: EDU lengths distribution</figcaption></figure>