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Title: Dialo-AP: A Dependency Parsing Based Argument Parser for Dialogues
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.  more » « less
Award ID(s):
2214070
PAR ID:
10441747
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 29th International Conference on Computational Linguistics (COLING)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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