@inproceedings{wang-etal-2021-generative-adversarial,
    title = "Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications",
    author = "Wang, Yuh-Shyang  and
      Chen, Chao-Yi  and
      Lee, Lung-Hao",
    editor = "Lee, Lung-Hao  and
      Chang, Chia-Hui  and
      Chen, Kuan-Yu",
    booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
    month = oct,
    year = "2021",
    address = "Taoyuan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.rocling-1.36/",
    pages = "280--285",
    abstract = "We propose the mixed-attention-based Generative Adversarial Network (named maGAN), and apply it for citation intent classification in scientific publication. We select domain-specific training data, propose a mixed-attention mechanism, and employ generative adversarial network architecture for pre-training language model and fine-tuning to the downstream multi-class classification task. Experiments were conducted on the SciCite datasets to compare model performance. Our proposed maGAN model achieved the best Macro-F1 of 0.8532."
}