@inproceedings{chai-etal-2022-improving,
    title = "Improving Multi-task Stance Detection with Multi-task Interaction Network",
    author = "Chai, Heyan  and
      Tang, Siyu  and
      Cui, Jinhao  and
      Ding, Ye  and
      Fang, Binxing  and
      Liao, Qing",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.emnlp-main.193/",
    doi = "10.18653/v1/2022.emnlp-main.193",
    pages = "2990--3000",
    abstract = "Stance detection aims to identify people`s standpoints expressed in the text towards a target, which can provide powerful information for various downstream tasks.Recent studies have proposed multi-task learning models that introduce sentiment information to boost stance detection.However, they neglect to explore capturing the fine-grained task-specific interaction between stance detection and sentiment tasks, thus degrading performance.To address this issue, this paper proposes a novel multi-task interaction network (MTIN) for improving the performance of stance detection and sentiment analysis tasks simultaneously.Specifically, we construct heterogeneous task-related graphs to automatically identify and adapt the roles that a word plays with respect to a specific task. Also, a multi-task interaction module is designed to capture the word-level interaction between tasks, so as to obtain richer task representations.Extensive experiments on two real-world datasets show that our proposed approach outperforms state-of-the-art methods in both stance detection and sentiment analysis tasks."
}