@inproceedings{li-etal-2017-bibi,
    title = "{BIBI} System Description: Building with {CNN}s and Breaking with Deep Reinforcement Learning",
    author = "Li, Yitong  and
      Cohn, Trevor  and
      Baldwin, Timothy",
    editor = "Bender, Emily  and
      Daum{\'e} III, Hal  and
      Ettinger, Allyson  and
      Rao, Sudha",
    booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-5404/",
    doi = "10.18653/v1/W17-5404",
    pages = "27--32",
    abstract = "This paper describes our submission to the sentiment analysis sub-task of ``Build It, Break It: The Language Edition (BIBI)'', on both the builder and breaker sides. As a builder, we use convolutional neural nets, trained on both phrase and sentence data. As a breaker, we use Q-learning to learn minimal change pairs, and apply a token substitution method automatically. We analyse the results to gauge the robustness of NLP systems."
}