Learning from Omission

Bill McDowell, Noah Goodman


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P19-1059.pdf