Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107876
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.creator | Xu, C | en_US |
dc.creator | Li, J | en_US |
dc.creator | Li, P | en_US |
dc.creator | Yang, M | en_US |
dc.date.accessioned | 2024-07-15T07:55:28Z | - |
dc.date.available | 2024-07-15T07:55:28Z | - |
dc.identifier.isbn | 978-1-959429-62-3 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107876 | - |
dc.description | The 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, July 9-14, 2023 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Association for Computational Linguistics (ACL) | en_US |
dc.rights | © 2023 Association for Computational Linguistics | en_US |
dc.rights | Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | 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/. | en_US |
dc.title | Topic-guided self-introduction generation for social media users | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 11387 | en_US |
dc.identifier.epage | 11402 | en_US |
dcterms.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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Findings of the Association for Computational Linguistics: ACL 2023, p. 11387–11402, Toronto, Canada. Association for Computational Linguistics | en_US |
dcterms.issued | 2023 | - |
dc.relation.conference | Annual Meeting of the Association for Computational Linguistics [ACL] | en_US |
dc.description.validate | 202407 bcwh | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a3033 | - |
dc.identifier.SubFormID | 49244 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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2023.findings-acl.722.pdf | 793.69 kB | Adobe PDF | View/Open |
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