@inproceedings{mcdowell-goodman-2019-learning,
title = "Learning from Omission",
author = "McDowell, Bill and
Goodman, Noah",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1059",
doi = "10.18653/v1/P19-1059",
pages = "619--628",
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.",
}
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%0 Conference Proceedings
%T Learning from Omission
%A McDowell, Bill
%A Goodman, Noah
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F mcdowell-goodman-2019-learning
%X 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.
%R 10.18653/v1/P19-1059
%U https://aclanthology.org/P19-1059
%U https://doi.org/10.18653/v1/P19-1059
%P 619-628
Markdown (Informal)
[Learning from Omission](https://aclanthology.org/P19-1059) (McDowell & Goodman, ACL 2019)
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.