A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition

• URL: http://arxiv.org/abs/2408.08971v3
• Date: Tue, 08 Jul 2025 15:18:26 GMT
• Title: A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition
• Authors: Nelson Filipe Costa, Leila Kosseim,
• Abstract summary: We propose a novel multi-label classification approach to implicit discourse relation recognition (IDRR)<n>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.<n>We conduct extensive experiments to identify optimal model configurations and loss functions in both settings.
• Score: 0.23020018305241333
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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