@inproceedings{shu-etal-2020-controllable,
title = "Controllable Text Generation with Focused Variation",
author = "Shu, Lei and
Papangelis, Alexandros and
Wang, Yi-Chia and
Tur, Gokhan and
Xu, Hu and
Feizollahi, Zhaleh and
Liu, Bing and
Molino, Piero",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.339",
doi = "10.18653/v1/2020.findings-emnlp.339",
pages = "3805--3817",
abstract = "This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from the difficulty of generating text according to the given attributes, to the lack of diversity of the generated texts. FVN addresses these issues by learning disjoint discrete latent spaces for each attribute inside codebooks, which allows for both controllability and diversity, while at the same time generating fluent text. We evaluate FVN on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations.",
}
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<abstract>This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from the difficulty of generating text according to the given attributes, to the lack of diversity of the generated texts. FVN addresses these issues by learning disjoint discrete latent spaces for each attribute inside codebooks, which allows for both controllability and diversity, while at the same time generating fluent text. We evaluate FVN on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations.</abstract>
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%0 Conference Proceedings
%T Controllable Text Generation with Focused Variation
%A Shu, Lei
%A Papangelis, Alexandros
%A Wang, Yi-Chia
%A Tur, Gokhan
%A Xu, Hu
%A Feizollahi, Zhaleh
%A Liu, Bing
%A Molino, Piero
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F shu-etal-2020-controllable
%X This work introduces Focused-Variation Network (FVN), a novel model to control language generation. The main problems in previous controlled language generation models range from the difficulty of generating text according to the given attributes, to the lack of diversity of the generated texts. FVN addresses these issues by learning disjoint discrete latent spaces for each attribute inside codebooks, which allows for both controllability and diversity, while at the same time generating fluent text. We evaluate FVN on two text generation datasets with annotated content and style, and show state-of-the-art performance as assessed by automatic and human evaluations.
%R 10.18653/v1/2020.findings-emnlp.339
%U https://aclanthology.org/2020.findings-emnlp.339
%U https://doi.org/10.18653/v1/2020.findings-emnlp.339
%P 3805-3817
Markdown (Informal)
[Controllable Text Generation with Focused Variation](https://aclanthology.org/2020.findings-emnlp.339) (Shu et al., Findings 2020)
ACL
- Lei Shu, Alexandros Papangelis, Yi-Chia Wang, Gokhan Tur, Hu Xu, Zhaleh Feizollahi, Bing Liu, and Piero Molino. 2020. Controllable Text Generation with Focused Variation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3805–3817, Online. Association for Computational Linguistics.