GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction

Junyoung Son, Jinsung Kim, Jungwoo Lim, Heuiseok Lim

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)

Abstract

The dialogue-based relation extraction (DialogRE) task aims to predict the relations between argument pairs that appear in dialogue. Most previous studies utilize fine-tuning pre-trained language models (PLMs) only with extensive features to supplement the low information density of the dialogue by multiple speakers. To effectively exploit inherent knowledge of PLMs without extra layers and consider scattered semantic cues on the relation between the arguments, we propose a Guiding model with RelAtional Semantics using Prompt (GRASP). We adopt a prompt-based fine-tuning approach and capture relational semantic clues of a given dialogue with 1) an argument-aware prompt marker strategy and 2) the relational clue detection task. In the experiments, GRASP achieves state-of-the-art performance in terms of both F1 and F1c scores on a DialogRE dataset even though our method only leverages PLMs without adding any extra layers.

Original languageEnglish
Pages (from-to)412-423
Number of pages12
JournalProceedings - International Conference on Computational Linguistics, COLING
Volume29
Issue number1
Publication statusPublished - 2022
Event29th International Conference on Computational Linguistics, COLING 2022 - Gyeongju, Korea, Republic of
Duration: 2022 Oct 122022 Oct 17

Bibliographical note

Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques), the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2018-0-01405) supervised by the IITP, and the MSIT, Korea, under the ICT Creative Consilience program (IITP-2022-2020-0-01819) supervised by the IITP.

Publisher Copyright:
© 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

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