DisCuT and DiscReT: MELODI at DISRPT 2025 Multilingual discourse segmentation, connective tagging and relation classification
Robin Pujol, Firmin Rousseau, Philippe Müller, Chloé Braud · 2025
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.