@inproceedings{xingren-etal-2025-tacl,
    title = "{T}a{CL}-{C}o{M}o{E}: Task-adaptive Contrastive Learning with Cooperative Mixture of Experts for Multi-task Social Media Analysis",
    author = "Xingren, Wang  and
      Liu, Hongde  and
      Liushanhong  and
      Meng, Feiyang  and
      He, Chenyuan  and
      Zhu, Senbin  and
      Zechen, Li  and
      Jia, Yuxiang",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.84/",
    pages = "1569--1581",
    ISBN = "979-8-89176-298-5",
    abstract = "Social media has become a crucial platform for information dissemination and opinion expression. The massive and continuous generation of user content has given rise to various natural language processing tasks, such as sentiment analysis and topic classification. However, existing mainstream approaches typically focus on modeling individual tasks in isolation, lacking systematic exploration of collaborative modeling across multiple tasks. This neglects the inherent correlations among social media tasks, thereby limiting the model{'}s ability to fully comprehend and exploit the rich, multi-dimensional semantic information embedded in text. To address this challenge, we propose $\textbf{Ta}$sk-adaptive $\textbf{C}$ontrastive $\textbf{L}$earning with $\textbf{Co}$operative $\textbf{M}$ixture $\textbf{o}$f $\textbf{E}$xperts ($\textbf{TaCL-CoMoE}$), a unified framework for social media multi-task learning. Specifically, we improve the gating mechanism by replacing the traditional softmax routing with sigmoid activation, enabling cooperative selection among multiple experts and mitigating the ``expert monopoly'' phenomenon. In addition, we introduce a task-adaptive contrastive learning strategy to further enhance the model{'}s ability to capture and distinguish semantic structures across different tasks. Experimental results on multiple public social media datasets demonstrate that TaCL-CoMoE consistently achieves state-of-the-art (SOTA) performance. The code is available at https://github.com/wxr2847/TaCL-CoMoE."
}