@inproceedings{golub-etal-2017-two,
    title = "Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension",
    author = "Golub, David  and
      Huang, Po-Sen  and
      He, Xiaodong  and
      Deng, Li",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D17-1087/",
    doi = "10.18653/v1/D17-1087",
    pages = "835--844",
    abstract = "We develop a technique for transfer learning in machine comprehension (MC) using a novel two-stage synthesis network. Given a high performing MC model in one domain, our technique aims to answer questions about documents in another domain, where we use no labeled data of question-answer pairs. Using the proposed synthesis network with a pretrained model on the SQuAD dataset, we achieve an F1 measure of 46.6{\%} on the challenging NewsQA dataset, approaching performance of in-domain models (F1 measure of 50.0{\%}) and outperforming the out-of-domain baseline by 7.6{\%}, without use of provided annotations."
}