Speaker-Aware Discourse Parsing on Multi-Party Dialogues

Nan Yu, Guohong Fu, Min Zhang


Abstract
Discourse parsing on multi-party dialogues is an important but difficult task in dialogue systems and conversational analysis. It is believed that speaker interactions are helpful for this task. However, most previous research ignores speaker interactions between different speakers. To this end, we present a speaker-aware model for this task. Concretely, we propose a speaker-context interaction joint encoding (SCIJE) approach, using the interaction features between different speakers. In addition, we propose a second-stage pre-training task, same speaker prediction (SSP), enhancing the conversational context representations by predicting whether two utterances are from the same speaker. Experiments on two standard benchmark datasets show that the proposed model achieves the best-reported performance in the literature. We will release the codes of this paper to facilitate future research.
Anthology ID:
2022.coling-1.477
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5372–5382
Language:
URL:
https://aclanthology.org/2022.coling-1.477
DOI:
Bibkey:
Cite (ACL):
Nan Yu, Guohong Fu, and Min Zhang. 2022. Speaker-Aware Discourse Parsing on Multi-Party Dialogues. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5372–5382, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Speaker-Aware Discourse Parsing on Multi-Party Dialogues (Yu et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.477.pdf
Code
 yunan4nlp/sa-dpmd
Data
Molweni