Discourse Relation Prediction and Discourse Parsing in Dialogues with Minimal Supervision - Méthodes et Ingénierie des Langues, des Ontologies et du Discours Access content directly
Conference Papers Year : 2024

Discourse Relation Prediction and Discourse Parsing in Dialogues with Minimal Supervision

Abstract

Discourse analysis plays a crucial role in Nat- ural Language Processing, with discourse re- lation prediction arguably being the most dif- ficult task in discourse parsing. Previous stud- ies have generally focused on explicit or im- plicit discourse relation classification in mono- logues, leaving dialogue an under-explored do- main. Facing the data scarcity issue, we pro- pose to leverage self-training strategies based on a Transformer backbone. Moreover, we design the first semi-supervised pipeline that sequentially predicts discourse structures and relations. Using 50 examples, our relation pre- diction module achieves 58.4 in accuracy on the STAC corpus, close to supervised state-of- the-art. Full parsing results show notable im- provements compared to the supervised mod- els both in-domain (gaming) and cross-domain (technical chat), with better stability.
Fichier principal
Vignette du fichier
Lisa_eacl2024__codi24.pdf (763.66 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04524155 , version 1 (27-03-2024)

Identifiers

  • HAL Id : hal-04524155 , version 1

Cite

Chuyuan Li, Chloé Braud, Maxime Amblard, Giuseppe Carenini. Discourse Relation Prediction and Discourse Parsing in Dialogues with Minimal Supervision. Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024), Mar 2024, Malte, Malta. pp.161--176. ⟨hal-04524155⟩
68 View
19 Download

Share

Gmail Mastodon Facebook X LinkedIn More