Abstract
Pragmatic reasoning allows humans to go beyond the literal meaning when interpret- ing language in context. Previous work has shown that such reasoning can improve the performance of already-trained language understanding systems. Here, we explore whether pragmatic reasoning during training can improve the quality of learned meanings. Our experiments on reference game data show that end-to-end pragmatic training produces more accurate utterance interpretation models, especially when data is sparse and language is complex.- Anthology ID:
- P19-1059
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 619–628
- Language:
- URL:
- https://aclanthology.org/P19-1059
- DOI:
- 10.18653/v1/P19-1059
- Cite (ACL):
- Bill McDowell and Noah Goodman. 2019. Learning from Omission. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 619–628, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Learning from Omission (McDowell & Goodman, ACL 2019)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/P19-1059.pdf