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http://hdl.handle.net/10397/107873
Title: | PopALM : popularity-aligned language models for social media trendy response prediction | Authors: | Yu, E Li, J Xu, X |
Issue Date: | 2024 | Source: | 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. | 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. | Keywords: | Popularity-aligned language models Trendy response prediction Curriculum learning |
Publisher: | ELRA Language Resources Association and the International Committee on Computational Linguistics | ISBN: | 978-2-493814-10-4 | Description: | The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 20-25 May, 2024, Torino, Italia | Rights: | © 2024 ELRA Language Resource Association: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/deed.en) 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/. |
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