Argument Generation with Retrieval, Planning, and Realization

Xinyu Hua, Zhe Hu, Lu Wang


Abstract
Automatic argument generation is an appealing but challenging task. In this paper, we study the specific problem of counter-argument generation, and present a novel framework, CANDELA. It consists of a powerful retrieval system and a novel two-step generation model, where a text planning decoder first decides on the main talking points and a proper language style for each sentence, then a content realization decoder reflects the decisions and constructs an informative paragraph-level argument. Furthermore, our generation model is empowered by a retrieval system indexed with 12 million articles collected from Wikipedia and popular English news media, which provides access to high-quality content with diversity. Automatic evaluation on a large-scale dataset collected from Reddit shows that our model yields significantly higher BLEU, ROUGE, and METEOR scores than the state-of-the-art and non-trivial comparisons. Human evaluation further indicates that our system arguments are more appropriate for refutation and richer in content.
Anthology ID:
P19-1255
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2661–2672
Language:
URL:
https://www.aclweb.org/anthology/P19-1255
DOI:
10.18653/v1/P19-1255
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1255.pdf
Video:
 https://vimeo.com/384728654