@inproceedings{yang-etal-2017-semi,
    title = "Semi-Supervised {QA} with Generative Domain-Adaptive Nets",
    author = "Yang, Zhilin  and
      Hu, Junjie  and
      Salakhutdinov, Ruslan  and
      Cohen, William",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/P17-1096/",
    doi = "10.18653/v1/P17-1096",
    pages = "1040--1050",
    abstract = "We study the problem of semi-supervised question answering{---}utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the \textit{Generative Domain-Adaptive Nets}. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the model-generated data distribution and the human-generated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text."
}