@inproceedings{huang-etal-2022-towards,
title = "Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation",
author = "Huang, Shulin and
Ma, Shirong and
Li, Yinghui and
Yangning, Li and
Lin, Shiyang and
Zheng, Haitao and
Shen, Ying",
editor = "Bosselut, Antoine and
Chandu, Khyathi and
Dhole, Kaustubh and
Gangal, Varun and
Gehrmann, Sebastian and
Jernite, Yacine and
Novikova, Jekaterina and
Perez-Beltrachini, Laura",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.gem-1.20",
doi = "10.18653/v1/2022.gem-1.20",
pages = "235--247",
abstract = "Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts. Facing this dilemma, we focus on a novel CTG scenario, i.e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e.g., objects and occasions). To promote the research on blessing generation, we present EBleT, a large-scale Entangled Blessing Text dataset containing 293K English sentences annotated with multiple attributes. Furthermore, we propose novel evaluation metrics to measure the quality of the blessing texts generated by the baseline models we designed. Our study opens a new research direction for controllable text generation and enables the development of attribute-entangled CTG models.",
}
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<abstract>Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts. Facing this dilemma, we focus on a novel CTG scenario, i.e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e.g., objects and occasions). To promote the research on blessing generation, we present EBleT, a large-scale Entangled Blessing Text dataset containing 293K English sentences annotated with multiple attributes. Furthermore, we propose novel evaluation metrics to measure the quality of the blessing texts generated by the baseline models we designed. Our study opens a new research direction for controllable text generation and enables the development of attribute-entangled CTG models.</abstract>
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%0 Conference Proceedings
%T Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation
%A Huang, Shulin
%A Ma, Shirong
%A Li, Yinghui
%A Yangning, Li
%A Lin, Shiyang
%A Zheng, Haitao
%A Shen, Ying
%Y Bosselut, Antoine
%Y Chandu, Khyathi
%Y Dhole, Kaustubh
%Y Gangal, Varun
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Novikova, Jekaterina
%Y Perez-Beltrachini, Laura
%S Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F huang-etal-2022-towards
%X Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts. Facing this dilemma, we focus on a novel CTG scenario, i.e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e.g., objects and occasions). To promote the research on blessing generation, we present EBleT, a large-scale Entangled Blessing Text dataset containing 293K English sentences annotated with multiple attributes. Furthermore, we propose novel evaluation metrics to measure the quality of the blessing texts generated by the baseline models we designed. Our study opens a new research direction for controllable text generation and enables the development of attribute-entangled CTG models.
%R 10.18653/v1/2022.gem-1.20
%U https://aclanthology.org/2022.gem-1.20
%U https://doi.org/10.18653/v1/2022.gem-1.20
%P 235-247
Markdown (Informal)
[Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation](https://aclanthology.org/2022.gem-1.20) (Huang et al., GEM 2022)
ACL
- Shulin Huang, Shirong Ma, Yinghui Li, Li Yangning, Shiyang Lin, Haitao Zheng, and Ying Shen. 2022. Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation. In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 235–247, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.