# DisCuT and DiscReT: MELODI at DISRPT 2025
Multilingual discourse segmentation, connective tagging
and relation classification

Robin Pujol, Firmin Rousseau, Philippe Muller, Chloé Braud

► To cite this version:

Robin Pujol, Firmin Rousseau, Philippe Muller, Chloé Braud. DisCuT and DiscReT: MELODI at DISRPT 2025 Multilingual discourse segmentation, connective tagging and relation classification. Proceedings of the 4th Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2025), Sep 2025, Suzhou, China. pp.21-35, 10.18653/v1/2025.disrpt-1.2. hal-05506536

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DisCuT and DiscReT: MELODI at DISRPT 2025
Multilingual discourse segmentation, connective tagging and relation classification

¹Robin Pujol* and ¹Firmin Rousseau* and <fnref n="1" />,³Philippe Muller and <fnref n="1" />,²,³Chloé Braud

¹UT - IRIT ; ²CNRS ; ³ANITI

¹firstname.lastname@irit.fr

Abstract

This paper presents the results obtained by the MELODI team for the three tasks proposed within the DISRPT 2025 shared task on discourse: segmentation, connective identification, and relation classification. The competition involves corpora in various languages, in several underlying frameworks, and datasets are given with or without sentence segmentation. This year, for the ranked, closed track, the campaign adds as a constraint to train only one model for each task, with an upper bound on the size of the model (no more than 4B parameters). An additional open track authorizes any size of, possibly non public, models that will not be reproduced by the organizers and thus not ranked. We compared several fine-tuning approaches either based on encoder-only transformer-based models, or auto-regressive generative ones. To be able to train one model on the variety of corpora, we explored various ways of combining data – by framework, language or language groups, with different sequential orderings –, and the addition of features to guide the model.

For the closed track, our final submitted system is based on XLM-RoBERTa large for relation identification, and on InfoXLM for segmentation and connective identification. Our experiments demonstrate that building a single, multilingual model does not necessarily degrade the performance compared to language-specific systems, with at best 64.06% for relation identification, 90.19% for segmentation and 81.15% for connective identification (on average on the development sets), results that are similar or higher than the ones obtained in previous campaigns. We also found that a generative approach could give even higher results on relation identification, with at best 64.65% on the dev sets.<fnref n="1" />

# 1 Introduction

Discourse parsing, the task consisting in finding semantic and rhetorical relations between spans of text (clauses, sentences, or paragraphs) is a well-known, yet challenging problem in computational linguistics, and has been shown to help in other NLP tasks, such as summarization (Zhang et al., 2023; Cripwell et al., 2023), question-answering (Fernandes et al., 2023; Jiang et al., 2023a), explainability (Devatine et al., 2023) or reasoning (Sharma et al., 2025). These relations reflect the argumentation structure or presentational choices, and have also been generalized to conversation, where dialog-specific phenomena such as adjacency pairs can be represented as relations between utterances – e.g., answer to a question, acknowledgment to a statement.

The field is characterized by significant divergence among different theoretical frameworks, with various views on the proper units of the structure, distinct typologies of relation, and heterogeneous formats for annotations. The DISRPT shared tasks have been aiming at a standardization of discourse-related tasks since 2019, by providing a unique format for data representation, and aligning intermediate objectives, such as discourse segmentation into basic units.

The 2025 edition goes a few steps beyond, by proposing a unified typology of 17 relations – over around 350 distinct labels originally and 191 in 2023 –, allowing to build systems across the varied annotation projects. The new campaign also integrates a few new corpora and new languages (Czech, Polish, Nigerian Pidgin), and take into account some datasets updates (e.g. the English PDTB split is modified to avoid overlap with the RST DT). Note that the label unification does not solve all discrepancies between datasets. A same pair of segments can be annotated with 2 distinct labels, as in (a) below: the same pair of sentences,

<footnote>*Equal contribution.</footnote>

<footnote n="1">¹Code segmentation/connective: https://gitlab.irit.fr/melodi/andiamo/discoursesegmentation/discut_disrpt25 ; relation: https://gitlab.irit.fr/melodi/andiamo/discourserelations/discret_disrpt25. Trained models are available on HuggingFace within the Discourse Hub organization: https://huggingface.co/multilingual-discourse-hub</footnote>
from the same document, is annotated in both the RST DT and the PDTB, but with different relations that also correspond to different labels in the DISRPT label set – resp. *contrast* and *alterna-tion*. This could come from differences between the frameworks, the annotation project or the anno-tators training. In addition, the divergence between annotations is also reflected in the segmentation de-cisions, and we may have two slightly different pair of segments with the same label, as in (b), which could also be confusing for the model. These exam-ples demonstrate that combining discourse datasets remains hard for automatic systems.

(a) [Call it a fad.] [Or call it the wave of the future.] RST DT : *contrast* ; PDTB : *explicit expansion.disjunction* (wsj_0633)

(b) [who was derided as a "tool-and-die man"]
[when GE brought him in to clean up Kidder in
1987] RST DT temporal ; PDTB : explicit tempo-
ral.synchronous (wsj_0604)

This year adds a few constraints to reflect the
recent evolution of NLP: instead of allowing for
separate models, it is mandatory to cast each of the
3 sub-tasks (discourse unit segmentation, connec-
tive recognition, relation labelling) into a multilin-
gual approach, with only one model trained on all
datasets available for the task. This makes for more
general model, and acknowledge the existence of
more and more non-monolingual models. With
the advent of "Large" pretrained Language Models
(PLM), questions of reproducibility or accessibil-
ity of resources are now also very important, and
the official track caps the size of pretrained model
to 4B parameters for what are expected to be the
main approaches: fine-tuning a large model or us-
ing an in-context learning approach in a generative
context.

In this work, we describe various comparisons
made between some of those choices, involving
fine-tuning moderately sized encoders or sequence-
to-sequence models, adding specific features to
guide the models, and considering different strate-
gies to manage the variety of data during training.
For all tasks, our best approach relies on fine-tuning
a PLM with a concatenation of the datasets with ad-
ditional features with the following overall results:
90.19% (parsed) / 86.89% (plain) for segmenta-
tion, 80.11% / 79.79 for connective detection, and
64.06% for relation, all on the development sets.
We also showed with the generative model (open
track) how a multilingual approach proves better
than fine-tuning separate models for relation label-

ing (64% on average on dev sets), confirming the
importance of multilingual pretrained models.

## 2 Related work

Segmentation and connective identification have
long been considered as easy and solved tasks, but
most existing studies were on English newswire
from the RST DT (Carlson et al., 2001). However,
performance drop for under-represented languages
and domains, or for dialogues, or when gold infor-
mation such as sentence split is not given (Braud
et al., 2017a; Scholman et al., 2021). Preliminary
studies made use of lexico-syntactic features, e.g.
(Lin et al., 2014) for connective or e.g. (Fisher and
Roark, 2007; Braud et al., 2017b) for segmenta-
tion, while more recent approaches rely on trans-
former architectures, mostly varying the pretrained
language model (PLM) used (Bakshi and Sharma,
2021; Metheniti et al., 2023; Lu et al., 2023).

Discourse relation classification is possibly the
most studied task, especially on the English PDTB
(Miltsakaki et al., 2004; Webber et al., 2019) with
a specific focus on implicit relations – where no ex-
plicit connective such as – e.g. *when, because,
if...then* – is used to mark the relation. In first
studies, the emphasis was on improving the rep-
resentation using e.g. linguistic features (Lin et al.,
2009), or data augmentation, especially based on
connective information, e.g. (Qin et al., 2017; Shi
et al., 2017). PLMs have allowed to increase per-
formance, especially for domain transfer (Shi and
Demberg, 2019), but there is still a large room for
improvements and many strategies have been pro-
posed, relying on extending contextual information
and external knowledge e.g. (Dai and Huang, 2018;
Liu et al., 2020; Dai and Huang, 2019), leverag-
ing explicit data or sense hierarchy, possibly with
contrastive learning e.g. (Kim et al., 2020; Liang
et al., 2020; Long and Webber, 2022), with also at-
tempts relying on additional pre-training of PLMs
(Kishimoto et al., 2020). Current state-of-the-art
on implicit discourse relation in PDTB3 is around
60% in F1 with a dedicated model relying on the
hierarchical organization of senses and the pres-
ence of implicit connectives in this dataset (Jiang
et al., 2023b). Some attempts have been made to
use large generative models such as ChatGPT for
the task, with, for now, very low scores in zero- or
few-shot settings (Yung et al., 2024).

DISRPT shared tasks allow to build and evaluate
models on a large range of languages, discourse
frameworks, domains and genres within an uni-
fied format (Zeldes et al., 2019, 2021; Braud et al.,
2023, 2024). Since the first edition, the benchmark
has grown in size and representativeness with now
39 datasets, 5 frameworks, 16 languages, several
domains and both monologues and dialogues (rep-
resented in 6 datasets in 2025 against 2 in 2023).

In all previous editions, the winning systems involved a fine-tuning approach of an encoder model on separate datasets. In 2023, the winning team for segmentation and connective identification (Metheniti et al., 2023) did use a multilingual model (XLM-RoBERTa large) but it was fine-tuned separately on each dataset. Their best results on the 2024 extended benchmark (Braud et al., 2024) are 92.14 in F₁ for segmentation (treebanked track<fnref n="2" />) and 82.73 for connective identification.

For relation classification, the same team pre-
sented results for a joint training over all datasets
with, however, performance behind the best system
(Liu et al., 2023) where several models were fine-
tuned – a single one for large datasets, and a joint
training on datasets from the same frameworks for
the others. The best results for relations are still
the ones obtained with the 2021 winning system,
DisCoDisCo (Gessler et al., 2021), also reported
in an extended version of the benchmark in (Braud
et al., 2024), with, at best, 62.21 mean accuracy.
Note that these results are not directly comparable
to ours, since the relation set has changed.

In 2023, one participating team proposed a generative approach (Anuranjana, 2023) with, however, results far behind the other systems. Recently, the DISRPT benchmark has been used to test discourse understanding of large language models (Eichin et al., 2025) using an unified set of relations – different from the ones proposed in DISRPT 2025 –, but even very large models, with more that 10B parameters, struggle with the task, with performance under 60% mean accuracy when using only a linear probe on top of the pretrained frozen models.

3 Data

We train and evaluate our models using all the
datasets provided by the shared task organizers.³
In total, the benchmark is composed of 39 datasets,
covering 13 languages and 6 frameworks. All the
corpora are listed in Appendix A.4. The format

and pre-processing steps are described in (Braud
et al., 2024). This year, 13 new datasets were added,
with now data for 3 new languages – Polish, Czech
and Nigerian Pidgin –, and 6 frameworks in total –
PDTB, RST, SDRT, eRST and ISO.

4 Global Approach

Our approach for all tasks relies on a pretrained
language model, based on a Transformer architec-
ture, fine-tuned on a concatenation of the datasets
in different steps. For each task, we report, for ref-
erence, the scores obtained with the same language
model fine-tuned on the separate datasets.

**Full concatenation:** Our reference / baseline system corresponds to the concatenation of all datasets, the model randomly draws instances from all datasets to form each batch during training. We then test different approaches meant to help the model to handle variations between annotations.

**Framework/language-based learning:** Here we merge the data in different steps, to test if a model could be better by learning separately the tasks for a single framework or group of languages before being introduced to a new one. The datasets for one framework / language group are concatenated and then learning is done sequentially on each group. The final performance correspond to the ones obtained at the end of sequential training, when all groups have been seen. We didn't have enough time to test all possible orders, but a few different options were investigated, as described below for each task. We also tested the injection of a subset of some datasets at the end of the first fine-tuning step, especially targeting datasets with very low results, as is done in certain continual pre-training techniques (Prabhu et al., 2023) to avoid catastrophic forgetting.

**Feature augmentation:** We implement some of the features tested in (Metheniti et al., 2024) to guide the model during joint training, more specifically language and framework information. Additional features for relation identification are indicated in Section 5.1. These information are given as additional tokens in the input, and ignored in the loss computation for sequential tasks.

**Fine-tuning a generative model (open track experiments):** We also compare our approach for relations to a quantized 4B LLM fine-tuned using a LoRA adapter (Hu et al., 2022), where the head

<footnote n="2">²Results on the Plain track are not indicated in the 2024 paper, but there were very similar to the treebanked track in 2023.
³https://github.com/disrpt/sharedtask2025</footnote>
of the language model is restricted to 17 tokens corresponding to the discourse relations to predict.

# 5 Segmentation

The segmentation task is a sequential learning task, with two possible labels – 'B' and 'O', resp. beginning or not an EDU (Elementary Discourse units, the minimal text segments to be linked by discourse relations). Two tracks are proposed in DISRPT 2025: the Treebanked track where sentence split and morpho-syntactic information are given – gold or predicted –, and the Plain track where the full documents have to be segmented from the provided tokenization. We optimized our approach on the Treebanked track, and the best system was retrained for the Plain track, with the preprocessing indicated below.

## 5.1 Settings for segmentation

**Preprocessing** For both tracks, we test the addition of features representing the language and the framework.

Additionally, for the Plain track (segmentation and connective), we need to split the documents into subsequences small enough for our models. Sentence information is very important for segmentation, so we tested a recent, high-performing sentence splitter, SaT – Segment any Text (Frohmann et al., 2024), available on HuggingFace.<fnref n="4" /> This model is multilingual, and designed for robustness across domains via a new pretraining scheme and additional fine-tuning. We found that the smaller version SaT-1L struggles with French quotation marks, a problem already noticed with Stanza, and thus chose the SaT-3L version. For the French corpus Annodis, when comparing the beginning of new sentences and the labels indicating the beginning of a new segment, we found that around 6% of the sentences do not correspond to a new segment in the original conllu data – split with stanza –, while only 2% are still not well segmented when using SaT3L. Even if the input is tokenized, some remapping was necessary afterwards to correspond to the input tokenization.

For dialogic datasets, this tool is far from perfect, so we adopted a simple cut-off strategy for LUNA as done in (Metheniti et al., 2023), but we experiment with SaT3L for the others.

**Model architecture** For segmentation and connective identification, contrary to discourse relation classification, we compare "small" models, under 560M parameters, as we consider these tasks as lower levels and thus believe that a faster and cheaper model should be favored.

**Training dataset construction** When combining datasets in the same language or group of languages, we tested sequential learning with the following order: CHINESE > ROM > GERM. The other datasets, and also some small datasets from these groups, are not part of the training set and predicted in zero-shot (e.g. for Farsi or Dutch) as we noticed it gave better results. CHINESE included all corpora in mandarin Chinese, ROM all romance languages (French, Spanish, Italian, Portuguese), GER all germanic languages (English, German).<fnref n="5" />

**Comparisons** As a (likely) upper bound, we fine-tuned one single system per dataset, as done in previous DISRPT campaigns, but using a multilingual pretrained model.

The reference system corresponds to the full concatenation, and we compared different multilingual pretrained models, all uploaded from HuggingFace: multilingual BERT base or large (Devlin et al.), RemBERT (Chung et al., 2021), XLM-Align (Chi et al., 2021b), and InfoXLM large (Chi et al., 2021a). Information on the size of the models is given in Table 1.

We compare models with or without features (language and framework), and a full concatenation vs framework grouping.

In a fully concatenated training scenario using mdeberta-v3-base, we tested models with language and/or framework features, as well as without any features.

<table><caption>Table 1: Size of the models tested for sequence classification: segmentation and connective identification.</caption><thead><tr><th>Model</th><th>Size</th><th># parameters</th><th># layers</th></tr></thead><tbody><tr><td>mBERT base</td><td>0.7 Go</td><td>110 M</td><td>12</td></tr><tr><td>XLM-Align</td><td>0.6 Go</td><td>125 M</td><td>12</td></tr><tr><td>InfoXLM large</td><td>2.7 Go</td><td>559 M</td><td>24</td></tr><tr><td>RemBERT</td><td>2.2 Go</td><td>575 M</td><td>36</td></tr></tbody></table>

**Hyper-parameters** Our models are fine-tuned with 30 epochs, a batch-size of 4, a learning rate of 10⁻<fnref n="5" />, the ADAMW optimizer, and training will

<footnote n="5">⁵The datasets only predicted in zero-shot are: eng.rst.oll, deu.rst.pcc, ces.rst.crdt, eus.rst.ert, fas.rst.prstc, nld.rst.nldt, por.rst.cstn, spa.rst.rststb.</footnote>

<footnote n="4">⁴https://huggingface.co/segment-any-text</footnote>
early stop if the monitored metric does not improve strictly for 10 consecutive evaluation steps. When using InfoXLM, the first 6th layers are frozen (out of 24). The models are optimized based on the mean F₁ over all datasets. The training for one model on all the datasets is around 2 hours and 40 minutes for the connective identification task, and 2 hours and 20 minutes for the segmentation task. The training has been done on a L40S GPU.

## 5.2 Results and analysis

All models are compared based on the results on the development set and fine-tuned with all datasets, including the surprise datasets.

### 5.2.1 Findings from preliminary experiments

**Learning with features or in sequence:** We report results of different comparisons using BERT in Table 2. As we can see from the Table, the use of additional features corresponding to the language and the framework seems to help the model, and thus they were kept for the subsequent experiments. On the other hand, grouping datasets per languages lead to a drop in performance (85.75) compared to full concatenation (89.85). Data reinjection, meaning continuing the fine-tuning but only on datasets corresponding to low performance,<fnref n="6" /> only leads to small improvement (86.12).

<table><caption>Table 2: Different segmentation settings with mBERT base fine-tuned on the concatenation of all datasets: 'no feat': no additional features; 'lang': a token added to represent the language; 'lang+fmk': additional tokens representing the language and the framework; or with sequential learning per language without features: 'grpd': grouping datasets as described in Section 5.1.</caption><thead><tr><th>Model</th><th>no feat</th><th>lang</th><th>lang+fmk</th><th>grpd</th></tr></thead><tbody><tr><td>mBERT base</td><td>89.85</td><td>90.10</td><td><strong>90.48</strong></td><td>85.75</td></tr></tbody></table>

**Models comparison** We compare different models for the setting giving the best performance on BERT – the full concatenation. We also use features indicating the language and framework when datasets are concatenated. As we can see in Table 3, all the models tested give very similar results, between 90.39 and 91.08% mean F₁, the best model being InfoXLM. The results obtained are also very close to the upper bound corresponding to single models, with at best 91.59. As noted in

(Braud et al., 2024), the performance seems to have reached a plateau on this task, and using separate or joint models give similar results when averaged over the datasets.

<table><caption>Table 3: Segmentation: Comparison between different models with single models and full concatenation, mean F₁ score over the datasets in the training+surprise release. The 'mean23' corresponds to the average score considering only the datasets present in DISRPT 2023.</caption><thead><tr><th></th><th>BERT large</th><th>RemBERT</th><th>XLM-Align</th><th>InfoXLM</th></tr></thead><tbody><tr><td colspan="5" style="text-align: center;">Single</td></tr><tr><td>mean</td><td>91.05</td><td>91.59</td><td>91.07</td><td>91.54</td></tr><tr><td>mean23</td><td>91.00</td><td>92.08</td><td>91.70</td><td>91.82</td></tr><tr><td colspan="5" style="text-align: center;">Concatenation</td></tr><tr><td>mean</td><td>90.17</td><td>90.76</td><td>90.39</td><td><strong>91.08</strong></td></tr><tr><td>mean23</td><td>90.98</td><td>90.96</td><td>90.97</td><td>91.25</td></tr></tbody></table>

### 5.2.2 Final results

Final results per dataset for segmentation are given in Table 4, with a full concatenation, language and framework features, and InfoXLM as pretrained language model. Even though the average between the 2023 and 2025 campaigns is the same, we can see a lot of differences in the datasets common to both campaigns. It's unclear why some corpora seem to benefit from the multilingual training while others are best when trained in isolation.

## 6 Connective identification

Connective identification is a sequential learning task, with three labels ('B', 'I', 'O', resp. beginning, inside, or outside a connective). We only test the best setting from the Segmentation task i.e. a full concatenation, with the language / framework features.

As for segmentation, our best results are obtained with InfoXLM (at best 80.47% mean F₁ in average (on dev). We found that freezing layers gave the best results with 6 layers frozen (vs 78.70 with all layers kept and 80.04 with 12 layers frozen). We obtained lower results with RemBERT (at best 79.18) and XLM-Align (at best 79.97). Our final results per dataset are given in Table 5. Again the averages for 2023 and 2025 are similar, with a lot of variance across common datasets.

## 7 Relation labeling

Discourse relation labeling is a multi-class classification task. The unification of the relation sets make it possible to jointly train over all datasets,

<footnote n="6">⁶The datasets reinjected are: fra.sdrt.annodis, spa.rst.sctb, zho.rst.gcdt, eng.rst.sts, zho.dep.scidtb, rus.rst.rrt, zho.rst.sctb.</footnote>
<table><caption>Table 4: Segmentation: Final results per datasets on the Treebanked and Plain tracks, on both dev and test sets. Results from DISRPT 2023 are reported from (Braud et al., 2023). The model is InfoXLM fine-tuned on a full concatenation of the datasets, with features indicating the language and framework in the input.</caption><thead><tr><th rowspan="2">Dataset</th><th colspan="3">Treebanked</th><th colspan="2">Plain</th></tr><tr><th>Dev</th><th>Test</th><th>Test23</th><th>Dev</th><th>Test</th><th>Test23</th></tr></thead><tbody><tr><td>ces.rst.crdt</td><td>92.42</td><td>94.04</td><td>-</td><td>91.35</td><td>89.94</td><td>-</td></tr><tr><td>deu.rst.pcc</td><td>96.48</td><td>93.75</td><td><strong>96.01</strong></td><td>93.31</td><td>92.89</td><td><strong>94.24</strong></td></tr><tr><td>*eng.dep.covdtb</td><td></td><td>90.13</td><td><strong>92.13</strong></td><td>91.32</td><td>92.00</td><td><strong>92.13</strong></td></tr><tr><td>eng.dep.scidtb</td><td>95.41</td><td>94.60</td><td><strong>95.07</strong></td><td>94.65</td><td><strong>94.52</strong></td><td>94.49</td></tr><tr><td>eng.erst.gentle</td><td>-</td><td>89.45</td><td>-</td><td>-</td><td>87.33</td><td>-</td></tr><tr><td>eng.erst.gum</td><td>95.12</td><td>90.76</td><td><strong>95.50</strong></td><td>93.20</td><td>93.74</td><td><strong>94.46</strong></td></tr><tr><td>eng.rst.oll</td><td>94.12</td><td>89.27</td><td>-</td><td>85.45</td><td>86.19</td><td>-</td></tr><tr><td>eng.rst.rstdt</td><td>95.80</td><td>95.99</td><td><strong>97.62</strong></td><td>94.75</td><td>94.55</td><td><strong>97.74</strong></td></tr><tr><td>eng.rst.sts</td><td>79.05</td><td>87.52</td><td>-</td><td>72.55</td><td>82.34</td><td>-</td></tr><tr><td>eng.rst.umuc</td><td>90.69</td><td>88.21</td><td>-</td><td>89.46</td><td>88.33</td><td>-</td></tr><tr><td>eng.sdrt.msdc</td><td>96.33</td><td>93.98</td><td>-</td><td>86.96</td><td>85.41</td><td>-</td></tr><tr><td>eng.sdrt.stac</td><td>93.73</td><td>91.01</td><td><strong>95.22</strong></td><td>88.31</td><td>87.49</td><td><strong>90.67</strong></td></tr><tr><td>eus.rst.ert</td><td>90.43</td><td><strong>91.08</strong></td><td>89.93</td><td>87.82</td><td>87.14</td><td><strong>91.09</strong></td></tr><tr><td>fas.rst.prstc</td><td>93.73</td><td><strong>93.76</strong></td><td>93.40</td><td>92.75</td><td>93.34</td><td><strong>93.36</strong></td></tr><tr><td>fra.sdrt.annodis</td><td>90.39</td><td>86.38</td><td><strong>88.21</strong></td><td>89.29</td><td>87.22</td><td><strong>90.89</strong></td></tr><tr><td>fra.sdrt summre</td><td></td><td>88.67</td><td>-</td><td>76.20</td><td>75.01</td><td>-</td></tr><tr><td>nld.rst.nldt</td><td>96.48</td><td><strong>96.73</strong></td><td>96.54</td><td>93.17</td><td>93.93</td><td><strong>97.19</strong></td></tr><tr><td>por.rst.cstn</td><td>92.71</td><td><strong>94.79</strong></td><td>93.98</td><td>91.26</td><td>91.53</td><td><strong>94.36</strong></td></tr><tr><td>rus.rst.rrt</td><td>92.41</td><td><strong>92.56</strong></td><td>85.58</td><td>90.50</td><td><strong>90.86</strong></td><td>85.41</td></tr><tr><td>spa.rst.rststb</td><td>94.52</td><td>92.04</td><td><strong>95.53</strong></td><td>92.68</td><td>91.06</td><td><strong>93.70</strong></td></tr><tr><td>spa.rst.sctb</td><td>88.68</td><td><strong>88.48</strong></td><td>85.63</td><td>72.52</td><td>80.67</td><td><strong>84.21</strong></td></tr><tr><td>zho.dep.scidtb</td><td>90.34</td><td>83.15</td><td><strong>89.07</strong></td><td>83.31</td><td>76.08</td><td><strong>90.04</strong></td></tr><tr><td>zho.rst.gcdt</td><td>89.30</td><td>90.29</td><td><strong>92.55</strong></td><td>87.99</td><td>88.73</td><td><strong>91.74</strong></td></tr><tr><td>zho.rst.sctb</td><td>64.52</td><td>67.91</td><td><strong>81.84</strong></td><td>53.76</td><td>55.09</td><td><strong>78.55</strong></td></tr><tr><td>mean</td><td>91.08</td><td>90.19</td><td>91.25</td><td>86.63</td><td>86.89</td><td>91.43</td></tr></tbody></table>

limiting label scarcity issues. However, some group of relations may be heterogeneous, the distribution of labels is imbalanced, with large differences between datasets. For this task, we present two set of experiments: the fine-tuning of an encoder-only model (closed track) and the fine-tuning with LoRA of a generative language model (open track).

## 7.1 Settings for relation classification

**Preprocessing** Within the closed track, we test the addition of features to inform the model with the language and framework of a specific instance. In addition, we encode the direction with specific tokens rather than switching units, as it was proved more efficient in (Metheniti et al., 2024). We also experiment with features indicating if the relation is inter or intra sentential by adding a token **local** or **non local**. With direction, locality and language/framework features, our input looks like, e.g. (from eng.dep.covdtb) : **English dep local this qualitative case study has investigated six issues < related to preparedness and response to MERS and poliomyelitis : {**

The input of our systems are pairs of segments,

<table><caption>Table 5: Connective: Final results per datasets on the Treebanked and Plain tracks, on both dev and test sets. Results from DISRPT 2023 are reported from (Braud et al., 2023). The model is InfoXLM-large fine-tuned on a full concatenation of the datasets, with features indicating the language and framework in the input.</caption><thead><tr><th rowspan="2">Dataset</th><th colspan="3">Treebanked</th><th colspan="2">Plain</th></tr><tr><th>Dev</th><th>Test</th><th>Test23</th><th>Dev</th><th>Test</th><th>Test23</th></tr></thead><tbody><tr><td>deu.pdtb.pcc</td><td>85.56</td><td>76.84</td><td>-</td><td>85.41</td><td>76.60</td><td>-</td></tr><tr><td>*eng.pdtb.gentle</td><td>-</td><td>87.69</td><td>-</td><td>-</td><td>86.41</td><td>-</td></tr><tr><td>eng.pdtb.gum</td><td>88.72</td><td>88.49</td><td>-</td><td>88.10</td><td>88.36</td><td>-</td></tr><tr><td>eng.pdtb.pdtb</td><td>92.52</td><td>93.59</td><td><strong>93.66</strong></td><td>93.04</td><td><strong>93.88</strong></td><td>91.64</td></tr><tr><td>*eng.pdtb.tedm</td><td>79.25</td><td><strong>79.09</strong></td><td>78.36</td><td>78.22</td><td><strong>78.80</strong></td><td>75.83</td></tr><tr><td>ita.pdtb.luna</td><td>73.94</td><td>64.65</td><td><strong>65.85</strong></td><td>67.42</td><td>61.92</td><td><strong>71.60</strong></td></tr><tr><td>pcm.pdtb.disconaija</td><td>71.03</td><td>78.51</td><td>-</td><td>68.27</td><td>77.68</td><td>-</td></tr><tr><td>pol.iso.pdc</td><td>67.45</td><td>70.01</td><td>-</td><td>63.84</td><td>70.08</td><td>-</td></tr><tr><td>por.pdtb.crp</td><td>85.02</td><td>80.04</td><td><strong>80.66</strong></td><td>81.48</td><td>78.00</td><td><strong>79.49</strong></td></tr><tr><td>*por.pdtb.tedm</td><td>80.91</td><td><strong>80.30</strong></td><td>80.29</td><td>75.49</td><td><strong>80.29</strong></td><td>79.45</td></tr><tr><td>tha.pdtb.tdtb</td><td>92.18</td><td><strong>90.36</strong></td><td>85.66</td><td>90.42</td><td><strong>89.70</strong></td><td>69.92</td></tr><tr><td>tur.pdtb.tdb</td><td>89.67</td><td>91.62</td><td><strong>92.77</strong></td><td>88.93</td><td><strong>92.85</strong></td><td>91.12</td></tr><tr><td>*tur.pdtb.tedm</td><td>62.67</td><td><strong>65.30</strong></td><td>64.10</td><td>65.09</td><td><strong>65.12</strong></td><td>64.78</td></tr><tr><td>zho.pdtb.cdtd</td><td>79.74</td><td>80.00</td><td><strong>89.00</strong></td><td>76.52</td><td>82.25</td><td><strong>90.38</strong></td></tr><tr><td>zho.pdtb.ted</td><td>77.89</td><td>75.10</td><td>-</td><td>79.01</td><td>74.93</td><td>-</td></tr><tr><td>mean</td><td>80.47</td><td>80.11</td><td>81.15</td><td>78.66</td><td><strong>79.79</strong></td><td>79.36</td></tr></tbody></table>

that could be longer than the maximal length of our models inputs. We truncate the input pairs if too long, by considering the whole length of the pair of arguments. Once tokenized, we compute the length of each unit, and truncate if the total length, combining both units, is larger than the max length of our model: if only one unit exceeds half of the max length, we truncate this unit at (max length - length of the other unit); if both units are longer than half of the max length, they are both truncated at the half of the max length.

**Model architecture** Contrary to the other tasks, we aim at testing bigger models, and we thus evaluate the fine-tuning of different pretrained multilingual models in the XLM-RoBERTa family until reaching the limitation of 4B parameters.

For the open track, we test only a large 4B model, and use LoRA for a faster training. More precisely, we use a decoder-only quantized 4B model (Qwen3-4B), finetuned with LoRA. The language model head is restricted to 17 tokens standing for each relation, where tokens are characters (from 'A' to 'Q'), to avoid issue with over generation. In inference model, we look at the probability given to these 17 tokens. The prompt is an instruction to pick a relation among the given list, given two textual segments. We found just keeping the instruction in English with no explicit mention of input languages worked better (example in Appendix A.2). We tested LoRA with rank 32 and 64, a batch of 64 for Nk steps (N=3 – 5 < 1 epoch), represent-
ing 65M of trainable parameters

**Training dataset formation** When combining datasets based on frameworks, we test the following orders:

- PDTB > SDRT > RST+ERST+DEP+ISO

- PDTB > SDRT+RST+ERST+DEP+ISO.

When combining datasets based on languages, we test the following order: ROM > GER > SLAV > fas > eus > zho > tur > tha, where upper letters indicate a group of languages and lower case indicate a single language (possibly covering several datasets). The list of languages in each group in indicated in Table 10 in Appendix.

Note that, due to time constraints, we only report results on the datasets in the regular release for these variations, not on the surprise datasets.

**Baselines / comparisons** As an upper bound, we trained separate models on each dataset with XLM-RoBERTa-base, to understand how a joint multilingual model fares against a specialized fine-tuning.

The reference joint system corresponds to the full concatenation, and we compare different sizes of the multilingual model XLM-RoBERTa, with possibly some layers frozen due to computational and time limitations: XLM-RoBERTa base (125M parameters), XLM-RoBERTa large (561M) and XLM-RoBERTa XL (3.48B). Our comparison are mainly done on the base model.

**Hyper-parameters** The XLM-RoBERTa models are fine-tuned with 10 epochs, a learning rate of $1^{-5}$, the ADAMW optimizer, and early stopping with a patience of 10 and a minimum delta of 0 and we evaluate it every 2000 steps. The first 6 layers are frozen when using XLM-RoBERTa large and XLM-RoBERTa base, and the first 18 with XLM-RoBERTa XL. We also adapt our batch size and gradient accumulation steps for XLM-RoBERTa large and XLM-RoBERTa base : we get a training batch size of 4 and a gradient accumulation of 4 but for XLM-RoBERTa XL we have a training batch size of 1 and a gradient accumulation of 16. The models are optimized based on the mean $F_1$ over all datasets. The training time is approximately 6 hours with XLM-RoBERTa base and 11 with large, and 62 with the XL version. We use one L40S GPU cards.

## 7.2 Results and analysis

**Sequential learning vs references** As described in Section 4, we compare a joint training to a

<table><caption>Table 6: Relation classification: mean accuracy of XLM-RoBERTa base (regular release, dev set). Systems are tested with and without features (framework, language, direction). Single: one model is fine-tuned on each dataset separately; Concat: all datasets are concatenated together; FMK: sequential learning based on frameworks (PDTB > SDRT > RST+ERST+DEP); LANG: sequential learning based on languages (ROM > GER > SLAVE > fas > ASIAN).</caption><thead><tr><th rowspan="2"></th><th colspan="4">Without feature</th><th colspan="4">With feature</th></tr><tr><th>Single</th><th>Concat</th><th>FMK</th><th>LANG</th><th>Single</th><th>Concat</th><th>FMK</th><th>LANG</th></tr></thead><tbody><tr><td>Mean</td><td>55.06</td><td>62.42</td><td>56.97</td><td>56.57</td><td>55.19</td><td><strong>62.54</strong></td><td>57.01</td><td>57.04</td></tr><tr><td>Mean23</td><td>55.81</td><td>63.33</td><td>58.40</td><td>56.60</td><td>55.90</td><td><strong>63.66</strong></td><td>58.37</td><td>56.88</td></tr></tbody></table>

sequential learning approach based on grouping datasets either by languages ('LANG') or frameworks ('FMK', order 1). Results are given in Table 6 in two settings: without any additional features, or with features indicating the language, the framework and the direction, see Section 4.

The 'Single' model was tested as an upper bound, since a separate model is fine-tuned, specialized on each dataset, as in most previous approaches to the task. In both configurations, the joint model reaches higher performance (at best 62.54% acc.) than the separate models (at best 55.19% acc.), demonstrating that datasets from different frameworks and languages can help each other. The unification of relation sets probably helps a lot here.

The sequential approaches do not outperform the full concatenation, neither by grouping frameworks nor languages. When looking at the performance at each step of sequential learning, the systems seem to forget crucial information for the datasets introduced at the beginning, with performance lowering for the initial groups. For the per framework approach, we also test a sequence PDTB > other frameworks, and reach 59.19 in accuracy against 57.01 with three groups of frameworks and 62.54 with a simple concatenation: making bigger groups only closes the gap with the full joint training.

The two sequential approaches give similar results, but we notice that the mean accuracy is better with the per framework approach when considering only the datasets present in 2023 ('mean23'). We tested with a reinjection, using XLM-RoBERTa base, of the Czech dataset – on which the performance are very low - at the end of the sequential learning: however, while with 1 additional epoch, the score on the Czech dataset increases – 43.9 vs 42.28 –, the performance drops if we continue to 10 epochs – 38.21 –, and the overall performance
are lower (10 epochs: 61.39 against 63.66).

<table><caption>Table 7: Relation classification: mean accuracy of XLM-RoBERTa base (regular release, dev set). Systems without features or using different set of features: 'lang': language, 'fmk': framework, 'dir': direction, 'lang/fmk': lang (resp. fmk) features for sequential learning on frameworks (resp. languages), 'loc': location.</caption><thead><tr><th></th><th>Without features</th><th>Lang+Fmk+Dir</th><th>Lang/Fmk+Dir</th><th>Lang+Fmk+Dir</th><th>Lang+Fmk+Dir+Loc</th></tr><tr><th></th><th>FMK</th><th>LANG</th><th>FMK</th><th>LANG</th><th>FMK</th><th>LANG</th></tr></thead><tbody><tr><th>Mean</th><td>56.97</td><td>56.57</td><td>57.01</td><td>57.04</td><td>56.12</td><td>56.35</td><td><strong>57.23</strong></td><td>56.51</td></tr><tr><th>Mean23</th><td>58.40</td><td>56.60</td><td>58.37</td><td>56.88</td><td>57.12</td><td>56.35</td><td><strong>58.43</strong></td><td>56.59</td></tr></tbody></table>

**Feature set** As we can see in Table 6, the additional features do not seem very helpful, with only limited gain for both reference and sequential approaches (+0.1%). In addition to tokens representing the language ('lang'), the framework ('fmk') and the direction of the relation ('dir'), we thus investigated the use of another feature representing the distance between the arguments – expressed as *local* for intra-sentential and *non local* for intersentential relations ('loc'). We also test the use of a feature indicating the language when learning per framework, and the other way around ('lang/fmk'). However, while adding features generally improve performance of our sequential approaches, the improvement remains very limited with the whole set of features tested, see Table 7.

**Size of the model** In the end, our best model was obtained with the full concatenation of datasets and features representing the language, the framework and the direction. Within this setting, we compare different sizes of the pretrained model. The results are presented in Table 8. As expected, the performance improves with larger models, but only slightly (+0.5% acc. between the base and large version), and even decreases with the largest one, maybe because of excessive freezing.

**Including surprise datasets** The final results are given in Table 9. We evaluate a 0-shot setting: the model trained only on the regular release is evaluated on the surprise datasets. As expected, the performance drop: from 62.54 to

<table><caption>Table 8: Relation classification: mean accuracy of XLM-RoBERTa base (regular release, dev set). Full concatenation and different model sizes.</caption><thead><tr><th></th><th>XLM-RoBERTa-base</th><th>XLM-RoBERTa-large</th><th>XLM-RoBERTa-XL</th></tr></thead><tbody><tr><th>Mean</th><td>62.54</td><td><strong>63.04</strong></td><td>62.38</td></tr><tr><th>Mean23</th><td>63.66</td><td><strong>63.76</strong></td><td>63.75</td></tr></tbody></table>

<table><caption>Table 9: Relation classification with full concatenation and base features (lang, fmk, dir): Mean accuracy including the surprise datasets, scores are on the development (DEV) or test (TEST) set. 'Zero-shot' is a system trained only on the regular release; 'Full FT' is a system fine-tuned on all data test (regular+surprise). The pre-trained language model is XLM-RoBERTa version base, large or XL. In bold, the best score per dataset.</caption><thead><tr><th rowspan="2">Dataset</th><th colspan="2">Zero-shot</th><th colspan="2">DEV</th><th colspan="2">TEST</th></tr><tr><th>Base</th><th>Large</th><th>Base</th><th>Large</th><th>Full FT</th><th>Full FT</th></tr><tr><th></th><th>Large</th><th>Test23</th><th></th><th>Large</th><th>Test23</th><th>Test23</th></tr></thead><tbody><tr><td>ces.rst.crdt</td><td>42.28</td><td>50.41</td><td>43.09</td><td><strong>55.28</strong></td><td>54.47</td><td>48.65</td></tr><tr><td><strong>deu.pdtb.pcc</strong></td><td>34.9</td><td>32.29</td><td>56.25</td><td>57.81</td><td>57.81</td><td>-</td></tr><tr><td>deu.rst.pcc</td><td>53.08</td><td><strong>55.0</strong></td><td>42.69</td><td>49.62</td><td>45.0</td><td>50.18</td></tr><tr><td>eng.dep.covdtb</td><td>69.57</td><td><strong>71.99</strong></td><td>68.7</td><td>65.65</td><td>63.86</td><td>67.25</td></tr><tr><td>eng.dep.scidtb</td><td>78.74</td><td>77.39</td><td>78.43</td><td><strong>79.93</strong></td><td>79.46</td><td>78.74</td></tr><tr><td>eng.erst.gentle</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td><strong>55.13</strong></td></tr><tr><td>eng.erst.gum</td><td>56.45</td><td>57.85</td><td>56.31</td><td>60.25</td><td>56.55</td><td><strong>64.1</strong></td></tr><tr><td>eng.pdtb.gentle</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td><strong>61.96</strong></td></tr><tr><td>eng.pdtb.gum</td><td>64.27</td><td>65.46</td><td>64.09</td><td>67.18</td><td>66.11</td><td><strong>68.18</strong></td></tr><tr><td>eng.pdtb.pdtb</td><td>71.36</td><td>71.96</td><td>71.86</td><td><strong>74.33</strong></td><td>73.57</td><td>73.71</td></tr><tr><td>eng.pdtb.tedm</td><td>58.99</td><td>58.43</td><td>60.11</td><td>60.67</td><td>60.11</td><td><strong>65.53</strong></td></tr><tr><td>eng.rst.oll</td><td>54.75</td><td>54.75</td><td>54.75</td><td>57.03</td><td><strong>57.79</strong></td><td>46.86</td></tr><tr><td>eng.rst.rstdt</td><td>62.12</td><td><strong>63.29</strong></td><td>59.1</td><td>60.33</td><td>60.27</td><td>60.65</td></tr><tr><td>eng.rst.sts</td><td>44.37</td><td><strong>45.77</strong></td><td>40.49</td><td>43.66</td><td>41.2</td><td>38.72</td></tr><tr><td>eng.rst.umuc</td><td>56.0</td><td>57.71</td><td>57.14</td><td>59.05</td><td>56.76</td><td><strong>60.33</strong></td></tr><tr><td>eng.sdrt.msde</td><td>84.45</td><td>84.99</td><td>84.63</td><td><strong>86.02</strong></td><td>84.59</td><td>85.16</td></tr><tr><td>eng.sdrt.stac</td><td>62.71</td><td>65.07</td><td>62.63</td><td>68.72</td><td>60.76</td><td><strong>70.74</strong></td></tr><tr><td>eus.rst.ert</td><td>53.26</td><td>54.89</td><td>53.75</td><td><strong>57.0</strong></td><td>56.03</td><td>54.23</td></tr><tr><td>fas.rst.prstc</td><td>55.91</td><td>53.31</td><td>53.31</td><td>55.51</td><td>56.31</td><td><strong>57.26</strong></td></tr><tr><td>fra.sdrt.annodis</td><td>61.95</td><td>58.51</td><td>59.66</td><td><strong>62.52</strong></td><td>61.19</td><td>59.74</td></tr><tr><td>ita.pdtb.luna</td><td>58.25</td><td>59.22</td><td>61.65</td><td>60.68</td><td>64.56</td><td><strong>65.6</strong></td></tr><tr><td>nld.rst.nldt</td><td>55.89</td><td>52.87</td><td>54.98</td><td>59.21</td><td>55.59</td><td><strong>62.15</strong></td></tr><tr><td><strong>pcm.pdtb.disconaija</strong></td><td>25.57</td><td>35.84</td><td>50.57</td><td>54.94</td><td>47.81</td><td><strong>57.92</strong></td></tr><tr><td><strong>pol.iso.pdc</strong></td><td>35.53</td><td>38.42</td><td>53.16</td><td><strong>58.55</strong></td><td>56.58</td><td>58.41</td></tr><tr><td>por.pdtb.crpcc</td><td>68.79</td><td>71.52</td><td>70.58</td><td>73.39</td><td>73.15</td><td><strong>77.96</strong></td></tr><tr><td>por.pdtb.tedm</td><td>58.42</td><td>57.89</td><td>57.89</td><td>63.16</td><td>62.63</td><td><strong>67.86</strong></td></tr><tr><td>por.rst.cstn</td><td>63.7</td><td>62.83</td><td>64.05</td><td>66.49</td><td><strong>68.41</strong></td><td>66.91</td></tr><tr><td>rus.rst.rrt</td><td>62.71</td><td>61.87</td><td>62.58</td><td>64.12</td><td>63.77</td><td><strong>66.75</strong></td></tr><tr><td>spa.rst.rststb</td><td>64.23</td><td>63.71</td><td>64.75</td><td><strong>69.19</strong></td><td>65.8</td><td>62.44</td></tr><tr><td>spa.rst.sctb</td><td>63.83</td><td>67.02</td><td>64.89</td><td><strong>69.15</strong></td><td>67.02</td><td>66.04</td></tr><tr><td>tha.pdtb.tdtb</td><td>95.49</td><td>96.14</td><td>95.25</td><td>96.14</td><td>95.9</td><td><strong>96.73</strong></td></tr><tr><td>tur.pdtb.tdb</td><td>50.8</td><td>52.73</td><td>56.27</td><td>58.84</td><td>52.73</td><td><strong>64.85</strong></td></tr><tr><td>tur.pdtb.tedm</td><td><strong>60.19</strong></td><td>56.4</td><td>56.87</td><td>56.4</td><td>55.92</td><td>59.23</td></tr><tr><td>zho.dep.scidtb</td><td>60.85</td><td>61.21</td><td>61.57</td><td>62.99</td><td>59.07</td><td><strong>67.44</strong></td></tr><tr><td>zho.pdtb.cdtd</td><td>80.7</td><td><strong>82.22</strong></td><td>79.06</td><td>81.29</td><td><strong>82.22</strong></td><td>78.63</td></tr><tr><td>zho.pdtb.ted</td><td>41.46</td><td>43.19</td><td>62.9</td><td>67.12</td><td>65.16</td><td><strong>67.67</strong></td></tr><tr><td>zho.rst.gdtt</td><td>63.02</td><td>64.81</td><td>62.13</td><td><strong>65.31</strong></td><td>61.13</td><td>62.85</td></tr><tr><td>zho.rst.sctb</td><td>61.7</td><td>58.51</td><td><strong>62.77</strong></td><td>58.51</td><td>56.38</td><td>52.83</td></tr><tr><td></td><td>59.34</td><td>60.15</td><td>61.36</td><td><strong>64.06</strong></td><td>62.38</td><td>64.01</td></tr><tr><td>Mean</td><td>63.57</td><td>63.72</td><td>63.15</td><td>65.36</td><td>63.75</td><td><strong>66.17</strong></td></tr></tbody></table>

59.34 for XLM-RoBERTa base, and from 63.04 to 60.15 for the large version. Unsurprisingly, the datasets with the lowest accuracy are the new ones. Some of them also correspond to new languages or frameworks, e.g. ces.rst.crdt, pol.iso.pdc and pcm.pdtb.disconaija. For others, it is more surprising, as the benchmark already contains similar data, e.g. deu.pdtb.pcc, eng.rst.oll, eng.rst.sts and zho.pdtb.ted. It could come from a lack of robustness of our system, or specific features of these datasets. Interestingly, performance are better in 0-shot for deu.rst.pcc (at best 55 against 45) which could indicate a form of over-fitting. Overall, when fine-tuned on the new set of data, scores are improved, reaching 64.06 mean accuracy with XLM-RoBERTa large on the dev set and 64.01 on the test set. We tested 2 runs with the large model, and
obtained stable results (mean= 63.88, std= 0.18).

We tested an even larger model – XLM-RoBERTa XL – without improving these scores, but note that, due to computational and time constraints, we froze 18 layers, possibly impeding its performance. Our final scores are a bit lower compared to 2023 (–1.5% on dev), due to the introduction of new, challenging datasets, such as the Czech ces.rst.crdt (at best 54.4% in acc), the English eng.rst.sts (at best, 45.77), and other datasets that remain difficult – deu.rst.pcc (at best 55).

## Results for the open track: generative model

The results obtained with the generative approach are reported in Table 11. While the model we used is fully open, can be fine-tuned locally, and is under the parameter count constraint, we consider it in the open track because reproducing the training will be difficult without a recent high-end GPU (not necessarily for RAM constraints, as it needs only 8GB but for various configuration issues; we ran it with an L40S GPU). The model has been uploaded to the huggingface hub,<fnref n="7" /> and an inference script is provided to verify predictions on all corpora. Training code is available. Mean average accuracy over the dataset (dev set only) is about 1% higher than our best model based on a decoder-only model, demonstrating the potential of this approach. Notably, training converges after less than one epoch over the concatenation of all datasets.

## 8 Conclusion

The MELODI team submitted systems for the DISRPT 2025 campaign for all tasks and setups: segmentation, connective identification, and relation classification. We explored various fine-tuning strategies for both encoder-only (closed track) and generative decoder-only (open track) models, and methods for combining diverse datasets across languages and frameworks. We show that training only one model on all data can achieve performance close to separate fine-tuning on each dataset, with even better results in the case of relation labelling. Given the time constraints a lot of potentially interesting ideas have not been fully explored and might be avenues for further progress.

## Acknowledgments

This work is supported by the AnDiaMO project (ANR-21-CE23-0020). Our work has benefited

from the AI Interdisciplinary Institute ANITI. ANITI is funded by the French "Investing for the Future – PIA3" program under the Grant agreement n°ANR-19-PI3A-0004. Chloé Braud and Philippe Muller are part of the program DesCartes and are also supported by the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program. The work was also supported by the ANR grant SUMM-RE (ANR-20-CE23-0017).

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# A Appendix

## A.1 Language groups

Table 10 shows which language we grouped for some of the experiments.

<table><caption>Table 10: Group of languages used in the sequential learning experiments for discourse relation identification. The datasets corresponding to Farsi, Basque, Chinese, Turkish and Thai are considered as specific groups.</caption><thead><tr><td>Abbrev.</td><td>language codes</td></tr></thead><tbody><tr><td>ROM</td><td>spa, por, ita, fra</td></tr><tr><td>GER</td><td>nld, deu, eng</td></tr><tr><td>SLAV</td><td>ces, rus</td></tr></tbody></table>

## A.2 Prompt example for the generative model

Here you have two discourse units of text prefixed with <argument1> and <argument2>:

* <argument1>: Cuando calienta el Sol

* <argument2>: En este artículo daremos una descripción general del Sol, de las capas que lo componen, de las estructuras que se observan y estudian en cada una de ellas, y de algunos de los fenómenos solares que inciden más directamente en nuestro planeta.

Classify the discourse relation between the two arguments using the following labels:

* class A: elaboration

* class B: attribution

* class C: conjunction

* class D: temporal

* class E: explanation

* class F: contrast

* class G: causal

* class H: purpose

* class I: comment

* class J: concession

* class K: condition

* class L: mode

* class M: organization

* class N: frame

* class O: query

* class P: reformulation

* class Q: alternation

### SOLUTION

The correct answer is: class M

## A.3 Generative approach: scores per dataset

The scores per dataset with the generative approach described in Section 7.1 are indicated in Table 11.
<table><caption>Table 11: Relation classification (open track): Results obtained with a generative model (Qwen4B) fine-tuned with LoRA. Results on the dev set on average for 2 runs of training. Test set was done only once on the last trained model (64.71 acc on the dev for this one). This is considered in the open track, but trained model can be found on huggingface hub under user Philippemuller, with a notebook reproducing the test inference and evaluation.</caption><thead><tr><th>corpus</th><th>dev</th><th>test</th></tr></thead><tbody><tr><td>Mean</td><td>64.65</td><td>65.54</td></tr><tr><td>ces.rst.crdt</td><td>47.97</td><td>50.00</td></tr><tr><td>deu.pdtb.pcc</td><td>60.42</td><td>65.46</td></tr><tr><td>deu.rst.pcc</td><td>42.11</td><td>54.58</td></tr><tr><td>eng.dep.covdtb</td><td>65.05</td><td>67.52</td></tr><tr><td>eng.dep.scidtb</td><td>83.13</td><td>81.26</td></tr><tr><td>eng.erst.gum</td><td>62.01</td><td>65.00</td></tr><tr><td>eng.pdtb.gentle</td><td>—</td><td>63.99</td></tr><tr><td>eng.pdtb.gum</td><td>67.60</td><td>68.71</td></tr><tr><td>eng.pdtb.pdtb</td><td>76.31</td><td>75.18</td></tr><tr><td>eng.pdtb.tedm</td><td>60.39</td><td>64.96</td></tr><tr><td>eng.rst.oll</td><td>55.70</td><td>47.97</td></tr><tr><td>eng.rst.rstdt</td><td>62.77</td><td>63.62</td></tr><tr><td>eng.rst.sts</td><td>47.36</td><td>46.34</td></tr><tr><td>eng.rst.umuc</td><td>61.00</td><td>61.36</td></tr><tr><td>eng.sdrt.msdc</td><td>88.15</td><td>86.61</td></tr><tr><td>eng.sdrt.stac</td><td>70.22</td><td>70.92</td></tr><tr><td>eus.rst.ert</td><td>52.03</td><td>51.34</td></tr><tr><td>fas.rst.prstc</td><td>56.71</td><td>56.76</td></tr><tr><td>fra.sdrt.annodis</td><td>64.53</td><td>60.23</td></tr><tr><td>ita.pdtb.luna</td><td>68.21</td><td>69.07</td></tr><tr><td>nld.rst.nldt</td><td>58.16</td><td>59.69</td></tr><tr><td>pcm.pdtb.disc</td><td>58.03</td><td>60.37</td></tr><tr><td>pol.iso.pdc</td><td>59.01</td><td>59.62</td></tr><tr><td>por.pdtb.crpc</td><td>74.16</td><td>77.80</td></tr><tr><td>por.pdtb.tedm</td><td>63.16</td><td>66.76</td></tr><tr><td>por.rst.cstn</td><td>68.59</td><td>71.32</td></tr><tr><td>rus.rst.rrt</td><td>65.38</td><td>66.68</td></tr><tr><td>spa.rst.rststb</td><td>70.24</td><td>65.02</td></tr><tr><td>spa.rst.sctb</td><td>69.68</td><td>69.18</td></tr><tr><td>tha.pdtb.tdtb</td><td>95.01</td><td>96.73</td></tr><tr><td>tur.pdtb.tdb</td><td>54.82</td><td>61.76</td></tr><tr><td>tur.pdtb.tedm</td><td>59.72</td><td>61.98</td></tr><tr><td>zho.dep.scidtb</td><td>70.11</td><td>70.23</td></tr><tr><td>zho.pdtb.cdtb</td><td>78.95</td><td>76.78</td></tr><tr><td>zho.pdtb.ted</td><td>66.62</td><td>67.97</td></tr><tr><td>zho.rst.gcdt</td><td>66.15</td><td>60.97</td></tr><tr><td>zho.rst.sctb</td><td>57.98</td><td>61.01</td></tr></tbody></table>

## A.4 Language Resources

The datasets were obtained from the following corpora: the Czech RST Discourse Treebank 1.0 (Poláková et al., 2023), the Potsdam Commentary Corpus (Stede and Neumann, 2014; Bourgonje and Stede, 2020), the COVID-19 Discourse Dependency Treebank (Nishida and Matsumoto, 2022), the Discourse Dependency TreeBank for Scientific Abstracts (Yang and Li, 2018; Yi et al., 2021; Cheng and Li, 2019), the Genre Tests for Linguistic Evaluation corpus (Aoyama et al., 2023), the Georgetown University Multilayer corpus (Zeldes, 2017), the RST Discourse Treebank (Carlson et al., 2001), the Science, Technology, and Society corpus (Potter, 2008), the University of Potsdam Multilayer UNSC Corpus (Zaczynska and Stede, 2024), the Minecraft Structured Dialogue Corpus (Thompson et al., 2024), the Strategic Conversations corpus (Asher et al., 2016), the Basque RST Treebank (Iruskieta et al., 2013), the Persian RST Corpus (Shahmohammadi et al., 2021), the ANNOTATION DISCursive corpus (Afantenos et al., 2012), the SUMM-RE corpus (Hunter et al., 2024; Prévot et al., 2025), the Dutch Discourse Treebank (Redeker et al., 2012), the Polish Discourse Corpus (Ogrodniczuk et al., 2024; Calzolari et al., 2024), the Cross-document Structure Theory News Corpus (Cardoso et al., 2011), the Russian RST Treebank (Toldova et al., 2017), the RST Spanish Treebank (da Cunha et al., 2011), the RST Spanish-Chinese Treebank (Cao et al., 2018), the Georgetown Chinese Discourse Treebank (Peng et al., 2022b,a), the DiscoNaija corpus (Scholman et al., 2025), the Penn Discourse Treebank (Prasad et al., 2014; Webber et al., 2019), the TED-Multilingual Discourse Bank (English) (Zeyrek et al., 2018, 2019), the LUNA Corpus Discourse Data Set (Tonelli et al., 2010; Riccardi et al., 2016), the Portuguese Discourse Bank (Mendes and Lejeune, 2022; Généreux et al., 2012), the Thai Discourse Treebank (Thai et al., 2022), the Turkish Discourse Bank (Zeyrek and Webber, 2008; Zeyrek and Kurfali, 2017), and the Chinese Discourse Treebank (Zhou et al., 2014).