Unsupervised Style Transfer in News Headlines via Discrete Style Space

Qianhui Liu, Yang Gao*, Yizhe Yang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The goal of headline style transfer in this paper is to make a headline more attractive while maintaining its meaning. The absence of parallel training data is one of the main problems in this field. In this work, we design a discrete style space for unsupervised headline style transfer, short for D-HST. This model decomposes the style-dependent text generation into content-feature extraction and style modelling. Then, generation decoder receives input from content, style, and their mixing components. In particular, it is considered that textual style signal is more abstract than the text itself. Therefore, we propose to model the style representation space as a discrete space, and each discrete point corresponds to a particular category of the styles that can be elicited by syntactic structure. Finally, we provide a new style-transfer dataset, named as TechST, which focuses on transferring news headline into those that are more eye-catching in technical social media. In the experiments, we develop two automatic evaluation metrics — style transfer rate (STR) and style-content trade-off (SCT) — along with a few traditional criteria to assess the overall effectiveness of the style transfer. In addition, the human evaluation is thoroughly conducted in terms of assessing the generation quality and creatively mimicking a scenario in which a user clicks on appealing headlines to determine the click-through rate. Our results indicate the D-HST achieves state-of-the-art results in these comprehensive evaluations.

源语言英语
主期刊名Chinese Computational Linguistics - 22nd China National Conference, CCL 2023, Proceedings
编辑Maosong Sun, Bing Qin, Xipeng Qiu, Jiang Jing, Xianpei Han, Gaoqi Rao, Yubo Chen
出版商Springer Science and Business Media Deutschland GmbH
91-105
页数15
ISBN(印刷版)9789819962068
DOI
出版状态已出版 - 2023
活动22nd China National Conference on Computational Linguistics, CCL 2023 - Harbin, 中国
期限: 3 8月 20235 8月 2023

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
14232 LNAI
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议22nd China National Conference on Computational Linguistics, CCL 2023
国家/地区中国
Harbin
时期3/08/235/08/23

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引用此

Liu, Q., Gao, Y., & Yang, Y. (2023). Unsupervised Style Transfer in News Headlines via Discrete Style Space. 在 M. Sun, B. Qin, X. Qiu, J. Jing, X. Han, G. Rao, & Y. Chen (编辑), Chinese Computational Linguistics - 22nd China National Conference, CCL 2023, Proceedings (页码 91-105). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 14232 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-6207-5_6