@inproceedings{zhang-etal-2021-dont,
title = "Don{'}t Change Me! User-Controllable Selective Paraphrase Generation",
author = "Zhang, Mohan and
Tan, Luchen and
Fu, Zihang and
Xiong, Kun and
Lin, Jimmy and
Li, Ming and
Tu, Zhengkai",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.307",
doi = "10.18653/v1/2021.eacl-main.307",
pages = "3522--3527",
abstract = "In the paraphrase generation task, source sentences often contain phrases that should not be altered. Which phrases, however, can be context dependent and can vary by application. Our solution to this challenge is to provide the user with explicit tags that can be placed around any arbitrary segment of text to mean {``}don{'}t change me!{''} when generating a paraphrase; the model learns to explicitly copy these phrases to the output. The contribution of this work is a novel data generation technique using distant supervision that allows us to start with a pretrained sequence-to-sequence model and fine-tune a paraphrase generator that exhibits this behavior, allowing user-controllable paraphrase generation. Additionally, we modify the loss during fine-tuning to explicitly encourage diversity in model output. Our technique is language agnostic, and we report experiments in English and Chinese.",
}
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<abstract>In the paraphrase generation task, source sentences often contain phrases that should not be altered. Which phrases, however, can be context dependent and can vary by application. Our solution to this challenge is to provide the user with explicit tags that can be placed around any arbitrary segment of text to mean “don’t change me!” when generating a paraphrase; the model learns to explicitly copy these phrases to the output. The contribution of this work is a novel data generation technique using distant supervision that allows us to start with a pretrained sequence-to-sequence model and fine-tune a paraphrase generator that exhibits this behavior, allowing user-controllable paraphrase generation. Additionally, we modify the loss during fine-tuning to explicitly encourage diversity in model output. Our technique is language agnostic, and we report experiments in English and Chinese.</abstract>
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%0 Conference Proceedings
%T Don’t Change Me! User-Controllable Selective Paraphrase Generation
%A Zhang, Mohan
%A Tan, Luchen
%A Fu, Zihang
%A Xiong, Kun
%A Lin, Jimmy
%A Li, Ming
%A Tu, Zhengkai
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2021-dont
%X In the paraphrase generation task, source sentences often contain phrases that should not be altered. Which phrases, however, can be context dependent and can vary by application. Our solution to this challenge is to provide the user with explicit tags that can be placed around any arbitrary segment of text to mean “don’t change me!” when generating a paraphrase; the model learns to explicitly copy these phrases to the output. The contribution of this work is a novel data generation technique using distant supervision that allows us to start with a pretrained sequence-to-sequence model and fine-tune a paraphrase generator that exhibits this behavior, allowing user-controllable paraphrase generation. Additionally, we modify the loss during fine-tuning to explicitly encourage diversity in model output. Our technique is language agnostic, and we report experiments in English and Chinese.
%R 10.18653/v1/2021.eacl-main.307
%U https://aclanthology.org/2021.eacl-main.307
%U https://doi.org/10.18653/v1/2021.eacl-main.307
%P 3522-3527
Markdown (Informal)
[Don’t Change Me! User-Controllable Selective Paraphrase Generation](https://aclanthology.org/2021.eacl-main.307) (Zhang et al., EACL 2021)
ACL
- Mohan Zhang, Luchen Tan, Zihang Fu, Kun Xiong, Jimmy Lin, Ming Li, and Zhengkai Tu. 2021. Don’t Change Me! User-Controllable Selective Paraphrase Generation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3522–3527, Online. Association for Computational Linguistics.