@inproceedings{madaan-etal-2020-politeness,
title = "Politeness Transfer: A Tag and Generate Approach",
author = "Madaan, Aman and
Setlur, Amrith and
Parekh, Tanmay and
Poczos, Barnabas and
Neubig, Graham and
Yang, Yiming and
Salakhutdinov, Ruslan and
Black, Alan W and
Prabhumoye, Shrimai",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.169/",
doi = "10.18653/v1/2020.acl-main.169",
pages = "1869--1881",
abstract = "This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at \url{https://github.com/tag-and-generate}."
}
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<abstract>This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate.</abstract>
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%0 Conference Proceedings
%T Politeness Transfer: A Tag and Generate Approach
%A Madaan, Aman
%A Setlur, Amrith
%A Parekh, Tanmay
%A Poczos, Barnabas
%A Neubig, Graham
%A Yang, Yiming
%A Salakhutdinov, Ruslan
%A Black, Alan W.
%A Prabhumoye, Shrimai
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F madaan-etal-2020-politeness
%X This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate.
%R 10.18653/v1/2020.acl-main.169
%U https://aclanthology.org/2020.acl-main.169/
%U https://doi.org/10.18653/v1/2020.acl-main.169
%P 1869-1881
Markdown (Informal)
[Politeness Transfer: A Tag and Generate Approach](https://aclanthology.org/2020.acl-main.169/) (Madaan et al., ACL 2020)
ACL
- Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, and Shrimai Prabhumoye. 2020. Politeness Transfer: A Tag and Generate Approach. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1869–1881, Online. Association for Computational Linguistics.