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        <title>A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition</title>
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        <namePart type="given">Nelson</namePart>
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            <namePart type="given">Frédéric</namePart>
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    <abstract>We propose a novel multi-label classification approach to implicit discourse relation recognition (IDRR). Our approach features a multi-task model that jointly learns multi-label representations of implicit discourse relations across all three sense levels in the PDTB 3.0 framework. The model can also be adapted to the traditional single-label IDRR setting by selecting the sense with the highest probability in the multi-label representation. We conduct extensive experiments to identify optimal model configurations and loss functions in both settings. Our approach establishes the first benchmark for multi-label IDRR and achieves SOTA results on single-label IDRR using DiscoGeM. Finally, we evaluate our model on the PDTB 3.0 corpus in the single-label setting, presenting the first analysis of transfer learning between the DiscoGeM and PDTB 3.0 corpora for IDRR.</abstract>
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%0 Conference Proceedings
%T A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition
%A Costa, Nelson Filipe
%A Kosseim, Leila
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F costa-kosseim-2025-multi-task
%X We propose a novel multi-label classification approach to implicit discourse relation recognition (IDRR). Our approach features a multi-task model that jointly learns multi-label representations of implicit discourse relations across all three sense levels in the PDTB 3.0 framework. The model can also be adapted to the traditional single-label IDRR setting by selecting the sense with the highest probability in the multi-label representation. We conduct extensive experiments to identify optimal model configurations and loss functions in both settings. Our approach establishes the first benchmark for multi-label IDRR and achieves SOTA results on single-label IDRR using DiscoGeM. Finally, we evaluate our model on the PDTB 3.0 corpus in the single-label setting, presenting the first analysis of transfer learning between the DiscoGeM and PDTB 3.0 corpora for IDRR.
%U https://aclanthology.org/2025.sigdial-1.18/
%P 231-245

Markdown (Informal)

[A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition](https://aclanthology.org/2025.sigdial-1.18/) (Costa & Kosseim, SIGDIAL 2025)

• A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition (Costa & Kosseim, SIGDIAL 2025)

ACL

• Nelson Filipe Costa and Leila Kosseim. 2025. A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition. In Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 231–245, Avignon, France. Association for Computational Linguistics.