Neural Argument Generation Augmented with Externally Retrieved Evidence

Xinyu Hua, Lu Wang


Abstract
High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than popular sequence-to-sequence generation models according to automatic evaluation and human assessments.
Anthology ID:
P18-1021
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
219–230
Language:
URL:
https://www.aclweb.org/anthology/P18-1021
DOI:
10.18653/v1/P18-1021
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-1021.pdf
Note:
 P18-1021.Notes.pdf
Video:
 https://vimeo.com/285800652
Presentation:
 P18-1021.Presentation.pdf