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
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 219–230
- Language:
- URL:
- https://aclanthology.org/P18-1021
- DOI:
- 10.18653/v1/P18-1021
- Cite (ACL):
- Xinyu Hua and Lu Wang. 2018. Neural Argument Generation Augmented with Externally Retrieved Evidence. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 219–230, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Neural Argument Generation Augmented with Externally Retrieved Evidence (Hua & Wang, ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P18-1021.pdf