@inproceedings{bao-qiao-2019-transfer,
    title = "Transfer Learning from Pre-trained {BERT} for Pronoun Resolution",
    author = "Bao, Xingce  and
      Qiao, Qianqian",
    editor = "Costa-juss{\`a}, Marta R.  and
      Hardmeier, Christian  and
      Radford, Will  and
      Webster, Kellie",
    booktitle = "Proceedings of the First Workshop on Gender Bias in Natural Language Processing",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-3812/",
    doi = "10.18653/v1/W19-3812",
    pages = "82--88",
    abstract = "The paper describes the submission of the team {\textquotedblleft}We used bert!{\textquotedblright} to the shared task Gendered Pronoun Resolution (Pair pronouns to their correct entities). Our final submission model based on the fine-tuned BERT (Bidirectional Encoder Representations from Transformers) ranks 14th among 838 teams with a multi-class logarithmic loss of 0.208. In this work, contribution of transfer learning technique to pronoun resolution systems is investigated and the gender bias contained in classification models is evaluated."
}