@inproceedings{tran-etal-2022-improving,
    title = "Improving Discriminative Learning for Zero-Shot Relation Extraction",
    author = "Tran, Van-Hien  and
      Ouchi, Hiroki  and
      Watanabe, Taro  and
      Matsumoto, Yuji",
    editor = "Das, Rajarshi  and
      Lewis, Patrick  and
      Min, Sewon  and
      Thai, June  and
      Zaheer, Manzil",
    booktitle = "Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge",
    month = may,
    year = "2022",
    address = "Dublin, Ireland and Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.spanlp-1.1",
    doi = "10.18653/v1/2022.spanlp-1.1",
    pages = "1--6",
    abstract = "Zero-shot relation extraction (ZSRE) aims to predict target relations that cannot be observed during training. While most previous studies have focused on fully supervised relation extraction and achieved considerably high performance, less effort has been made towards ZSRE. This study proposes a new model incorporating discriminative embedding learning for both sentences and semantic relations. In addition, a self-adaptive comparator network is used to judge whether the relationship between a sentence and a relation is consistent. Experimental results on two benchmark datasets showed that the proposed method significantly outperforms the state-of-the-art methods.",
}