Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107873
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.creator | Yu, E | en_US |
dc.creator | Li, J | en_US |
dc.creator | Xu, X | en_US |
dc.date.accessioned | 2024-07-15T07:55:27Z | - |
dc.date.available | 2024-07-15T07:55:27Z | - |
dc.identifier.isbn | 978-2-493814-10-4 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107873 | - |
dc.description | The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 20-25 May, 2024, Torino, Italia | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELRA Language Resources Association and the International Committee on Computational Linguistics | en_US |
dc.rights | © 2024 ELRA Language Resource Association: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/deed.en) | en_US |
dc.rights | The following publication Erxin Yu, Jing Li, and Chunpu Xu. 2024. PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12867–12878, Torino, Italia. ELRA and ICCL is available at https://aclanthology.org/2024.lrec-main.1127/. | en_US |
dc.subject | Popularity-aligned language models | en_US |
dc.subject | Trendy response prediction | en_US |
dc.subject | Curriculum learning | en_US |
dc.title | PopALM : popularity-aligned language models for social media trendy response prediction | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 12867 | en_US |
dc.identifier.epage | 12878 | en_US |
dcterms.abstract | Social media platforms are daily exhibiting millions of events. To preliminarily predict the mainstream public reaction to these events, we study trendy response prediction to automatically generate top-liked user replies to social media events. While previous works focus on generating responses without factoring in popularity, we propose Popularity-Aligned Language Models (PopALM) to distinguish responses liked by a larger audience through reinforcement learning. Recognizing the noisy labels from user “likes”, we tailor-make curriculum learning in proximal policy optimization (PPO) to help models capture the essential samples for easy-to-hard training. In experiments, we build a large-scale Weibo dataset for trendy response prediction, and its results show that PopALM can help boost the performance of advanced language models. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), p. 12867–12878, Torino, Italia. ELRA and ICCL. | en_US |
dcterms.issued | 2024 | - |
dc.relation.ispartofbook | Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) | en_US |
dc.description.validate | 202407 bcwh | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a3031 | - |
dc.identifier.SubFormID | 49236 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Conference Paper |
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2024.lrec-main.1127.pdf | 627.03 kB | Adobe PDF | View/Open |
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