@inproceedings{nangi-etal-2021-counterfactuals,
title = "Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer",
author = "Nangi, Sharmila Reddy and
Chhaya, Niyati and
Khosla, Sopan and
Kaushik, Nikhil and
Nyati, Harshit",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.7",
doi = "10.18653/v1/2021.acl-short.7",
pages = "40--48",
abstract = "Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a {`}what-if{'} scenario. This simple and disciplined approach also enables a fine-grained control on the transfer strength. We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.",
}
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<abstract>Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a ‘what-if’ scenario. This simple and disciplined approach also enables a fine-grained control on the transfer strength. We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.</abstract>
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%0 Conference Proceedings
%T Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer
%A Nangi, Sharmila Reddy
%A Chhaya, Niyati
%A Khosla, Sopan
%A Kaushik, Nikhil
%A Nyati, Harshit
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F nangi-etal-2021-counterfactuals
%X Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a ‘what-if’ scenario. This simple and disciplined approach also enables a fine-grained control on the transfer strength. We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.
%R 10.18653/v1/2021.acl-short.7
%U https://aclanthology.org/2021.acl-short.7
%U https://doi.org/10.18653/v1/2021.acl-short.7
%P 40-48
Markdown (Informal)
[Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer](https://aclanthology.org/2021.acl-short.7) (Nangi et al., ACL-IJCNLP 2021)
ACL
- Sharmila Reddy Nangi, Niyati Chhaya, Sopan Khosla, Nikhil Kaushik, and Harshit Nyati. 2021. Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 40–48, Online. Association for Computational Linguistics.