@inproceedings{samanta-etal-2021-hierarchical,
title = "A Hierarchical {VAE} for Calibrating Attributes while Generating Text using Normalizing Flow",
author = "Samanta, Bidisha and
Agrawal, Mohit and
Ganguly, NIloy",
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 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.187",
doi = "10.18653/v1/2021.acl-long.187",
pages = "2405--2415",
abstract = "In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.",
}
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<abstract>In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.</abstract>
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%0 Conference Proceedings
%T A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow
%A Samanta, Bidisha
%A Agrawal, Mohit
%A Ganguly, NIloy
%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 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F samanta-etal-2021-hierarchical
%X In this digital age, online users expect personalized content. To cater to diverse group of audiences across online platforms it is necessary to generate multiple variants of same content with differing degree of characteristics (sentiment, style, formality, etc.). Though text-style transfer is a well explored related area, it focuses on flipping the style attribute polarity instead of regulating a fine-grained attribute transfer. In this paper we propose a hierarchical architecture for finer control over the at- tribute, preserving content using attribute dis- entanglement. We demonstrate the effective- ness of the generative process for two different attributes with varied complexity, namely sentiment and formality. With extensive experiments and human evaluation on five real-world datasets, we show that the framework can generate natural looking sentences with finer degree of control of intensity of a given attribute.
%R 10.18653/v1/2021.acl-long.187
%U https://aclanthology.org/2021.acl-long.187
%U https://doi.org/10.18653/v1/2021.acl-long.187
%P 2405-2415
Markdown (Informal)
[A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow](https://aclanthology.org/2021.acl-long.187) (Samanta et al., ACL-IJCNLP 2021)
ACL