@inproceedings{zhou-etal-2021-encoding,
    title = "Encoding Explanatory Knowledge for Zero-shot Science Question Answering",
    author = "Zhou, Zili  and
      Valentino, Marco  and
      Landers, Donal  and
      Freitas, Andr{\'e}",
    editor = "Zarrie{\ss}, Sina  and
      Bos, Johan  and
      van Noord, Rik  and
      Abzianidze, Lasha",
    booktitle = "Proceedings of the 14th International Conference on Computational Semantics (IWCS)",
    month = jun,
    year = "2021",
    address = "Groningen, The Netherlands (online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.iwcs-1.5/",
    pages = "38--50",
    abstract = "This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task."
}