@inproceedings{holtzman-etal-2018-learning,
title = "Learning to Write with Cooperative Discriminators",
author = "Holtzman, Ari and
Buys, Jan and
Forbes, Maxwell and
Bosselut, Antoine and
Golub, David and
Choi, Yejin",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1152",
doi = "10.18653/v1/P18-1152",
pages = "1638--1649",
abstract = "Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that can guide a base RNN generator towards more globally coherent generations. More concretely, discriminators each specialize in a different principle of communication, such as Grice{'}s maxims, and are collectively combined with the base RNN generator through a composite decoding objective. Human evaluation demonstrates that text generated by our model is preferred over that of baselines by a large margin, significantly enhancing the overall coherence, style, and information of the generations.",
}
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<abstract>Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that can guide a base RNN generator towards more globally coherent generations. More concretely, discriminators each specialize in a different principle of communication, such as Grice’s maxims, and are collectively combined with the base RNN generator through a composite decoding objective. Human evaluation demonstrates that text generated by our model is preferred over that of baselines by a large margin, significantly enhancing the overall coherence, style, and information of the generations.</abstract>
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%0 Conference Proceedings
%T Learning to Write with Cooperative Discriminators
%A Holtzman, Ari
%A Buys, Jan
%A Forbes, Maxwell
%A Bosselut, Antoine
%A Golub, David
%A Choi, Yejin
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F holtzman-etal-2018-learning
%X Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that can guide a base RNN generator towards more globally coherent generations. More concretely, discriminators each specialize in a different principle of communication, such as Grice’s maxims, and are collectively combined with the base RNN generator through a composite decoding objective. Human evaluation demonstrates that text generated by our model is preferred over that of baselines by a large margin, significantly enhancing the overall coherence, style, and information of the generations.
%R 10.18653/v1/P18-1152
%U https://aclanthology.org/P18-1152
%U https://doi.org/10.18653/v1/P18-1152
%P 1638-1649
Markdown (Informal)
[Learning to Write with Cooperative Discriminators](https://aclanthology.org/P18-1152) (Holtzman et al., ACL 2018)
ACL
- Ari Holtzman, Jan Buys, Maxwell Forbes, Antoine Bosselut, David Golub, and Yejin Choi. 2018. Learning to Write with Cooperative Discriminators. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1638–1649, Melbourne, Australia. Association for Computational Linguistics.