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http://hdl.handle.net/10397/107876
Title: | Topic-guided self-introduction generation for social media users | Authors: | Xu, C Li, J Li, P Yang, M |
Issue Date: | 2023 | Source: | In Findings of the Association for Computational Linguistics: ACL 2023, p. 11387–11402, Toronto, Canada. Association for Computational Linguistics | Abstract: | Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user’s personal interests. While most prior work profiling users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user’s tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user’s history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling. | Publisher: | Association for Computational Linguistics (ACL) | ISBN: | 978-1-959429-62-3 | Description: | The 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, July 9-14, 2023 | Rights: | © 2023 Association for Computational Linguistics Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). The following publication Chunpu Xu, Jing Li, Piji Li, and Min Yang. 2023. Topic-Guided Self-Introduction Generation for Social Media Users. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11387–11402, Toronto, Canada. Association for Computational Linguistics is available at https://aclanthology.org/2023.findings-acl.722/. |
Appears in Collections: | Conference Paper |
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