Abstract
Understanding contrastive opinions is a key component of argument generation. Central to an argument is the claim, a statement that is in dispute. Generating a counter-argument then requires generating a response in contrast to the main claim of the original argument. To generate contrastive claims, we create a corpus of Reddit comment pairs self-labeled by posters using the acronym FTFY (fixed that for you). We then train neural models on these pairs to edit the original claim and produce a new claim with a different view. We demonstrate significant improvement over a sequence-to-sequence baseline in BLEU score and a human evaluation for fluency, coherence, and contrast.- Anthology ID:
- N19-1174
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1756–1767
- Language:
- URL:
- https://aclanthology.org/N19-1174
- DOI:
- 10.18653/v1/N19-1174
- Cite (ACL):
- Christopher Hidey and Kathy McKeown. 2019. Fixed That for You: Generating Contrastive Claims with Semantic Edits. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1756–1767, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Fixed That for You: Generating Contrastive Claims with Semantic Edits (Hidey & McKeown, NAACL 2019)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/N19-1174.pdf
- Code
- chridey/fixedthat