Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues
Résumé
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.
Domaines
Informatique [cs]| Origine | Fichiers produits par l'(les) auteur(s) |
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