DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition

Bowen Xing, Ivor Tsang

Abstract
The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog. In this paper, we put forward a new framework which models the explicit dependencies via integrating prediction-level interactions other than semantics-level interactions, more consistent with human intuition.Besides, we propose a speaker-aware temporal graph (SATG) and a dual-task relational temporal graph (DRTG) to introduce temporal relations into dialog understanding and dual-task reasoning. To implement our framework, we propose a novel model dubbed DARER, which first generates the context-, speaker- and temporal-sensitive utterance representations via modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions.Experiment results show that DARER outperforms existing models by large margins while requiring much less computation resource and costing less training time.Remarkably, on DSC task in Mastodon, DARER gains a relative improvement of about 25% over previous best model in terms of F1, with less than 50% parameters and about only 60% required GPU memory.
Anthology ID:
2022.findings-acl.286
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3611–3621
Language:
URL:
https://preview.aclanthology.org/icon-24-ingestion/2022.findings-acl.286/
DOI:
10.18653/v1/2022.findings-acl.286
Bibkey:
Cite (ACL):
Bowen Xing and Ivor Tsang. 2022. DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3611–3621, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition (Xing & Tsang, Findings 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/icon-24-ingestion/2022.findings-acl.286.pdf
Code
 xingbowen714/darer
Data
DailyDialog
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