Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues

Sougata Saha, Souvik Das, Rohini K. Srihari


Abstract
While neural approaches to argument mining (AM) have advanced considerably, most of the recent work has been limited to parsing monologues. With an urgent interest in the use of conversational agents for broader societal applications, there is a need to advance the state-of-the-art in argument parsers for dialogues. This enables progress towards more purposeful conversations involving persuasion, debate and deliberation. This paper discusses Dialo-AP, an end-to-end argument parser that constructs argument graphs from dialogues. We formulate AM as dependency parsing of elementary and argumentative discourse units; the system is trained using extensive pre-training and curriculum learning comprising nine diverse corpora. Dialo-AP is capable of generating argument graphs from dialogues by performing all sub-tasks of AM. Compared to existing state-of-the-art baselines, Dialo-AP achieves significant improvements across all tasks, which is further validated through rigorous human evaluation.
Anthology ID:
2022.coling-1.74
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:
887–901
Language:
URL:
https://aclanthology.org/2022.coling-1.74
DOI:
Bibkey:
Cite (ACL):
Sougata Saha, Souvik Das, and Rohini K. Srihari. 2022. Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues. In Proceedings of the 29th International Conference on Computational Linguistics, pages 887–901, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues (Saha et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.74.pdf
Code
 sougata-ub/dialo-ap
Data
CDCP