Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues - Université de Lorraine Access content directly
Conference Papers Year : 2023

Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues

Chuyuan Li
Patrick Huber
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  • PersonId : 1237763
Wen Xiao
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  • PersonId : 1237764
Maxime Amblard
Giuseppe Carenini
  • Function : Author
  • PersonId : 1237765

Abstract

Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs). We investigate multiple tasks for fine-tuning and show that the dialogue-tailored Sentence Ordering task performs best. To locate and exploit discourse information in PLMs, we propose an unsupervised and a semi-supervised method. Our proposals thereby achieve encouraging results on the STAC corpus, with F 1 scores of 57.2 and 59.3 for the unsupervised and semisupervised methods, respectively. When restricted to projective trees, our scores improved to 63.3 and 68.1.
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hal-04031267 , version 1 (15-06-2023)

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Chuyuan Li, Patrick Huber, Wen Xiao, Maxime Amblard, Chloé Braud, et al.. Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues. European Chapter of the Association for Computational Linguistics (EACL), May 2023, Dubrovnik, Croatia. pp.2562-2579. ⟨hal-04031267⟩
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