@inproceedings{lu-etal-2019-look,
    title = "Look-up and Adapt: A One-shot Semantic Parser",
    author = "Lu, Zhichu  and
      Arabshahi, Forough  and
      Labutov, Igor  and
      Mitchell, Tom",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D19-1104/",
    doi = "10.18653/v1/D19-1104",
    pages = "1129--1139",
    abstract = "Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited {\textquotedblleft}supported{\textquotedblright} domain of discourse and fail drastically when faced with an out-of-domain utterance, mainly due to the limitations of their semantic parser. In this paper, we propose a semantic parser that generalizes to out-of-domain examples by learning a general strategy for parsing an unseen utterance through adapting the logical forms of seen utterances, instead of learning to generate a logical form from scratch. Our parser maintains a memory consisting of a representative subset of the seen utterances paired with their logical forms. Given an unseen utterance, our parser works by looking up a similar utterance from the memory and adapting its logical form until it fits the unseen utterance. Moreover, we present a data generation strategy for constructing utterance-logical form pairs from different domains. Our results show an improvement of up to 68.8{\%} on one-shot parsing under two different evaluation settings compared to the baselines."
}