@inproceedings{brantley-etal-2019-non,
title = "Non-Monotonic Sequential Text Generation",
author = "Brantley, Kiante and
Cho, Kyunghyun and
Daum{\'e}, Hal and
Welleck, Sean",
editor = "Axelrod, Amittai and
Yang, Diyi and
Cunha, Rossana and
Shaikh, Samira and
Waseem, Zeerak",
booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3620",
pages = "57--59",
abstract = "Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy{'}s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order while achieving competitive performance with conventional left-to-right generation.",
}
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<abstract>Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order while achieving competitive performance with conventional left-to-right generation.</abstract>
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%0 Conference Proceedings
%T Non-Monotonic Sequential Text Generation
%A Brantley, Kiante
%A Cho, Kyunghyun
%A Daumé, Hal
%A Welleck, Sean
%Y Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F brantley-etal-2019-non
%X Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order while achieving competitive performance with conventional left-to-right generation.
%U https://aclanthology.org/W19-3620
%P 57-59
Markdown (Informal)
[Non-Monotonic Sequential Text Generation](https://aclanthology.org/W19-3620) (Brantley et al., WiNLP 2019)
ACL
- Kiante Brantley, Kyunghyun Cho, Hal Daumé, and Sean Welleck. 2019. Non-Monotonic Sequential Text Generation. In Proceedings of the 2019 Workshop on Widening NLP, pages 57–59, Florence, Italy. Association for Computational Linguistics.