@inproceedings{wu-etal-2023-ambiguous,
    title = "Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing",
    author = "Wu, Shan  and
      Xin, Chunlei  and
      Lin, Hongyu  and
      Han, Xianpei  and
      Liu, Cao  and
      Chen, Jiansong  and
      Yang, Fan  and
      Wan, Guanglu  and
      Sun, Le",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.787",
    doi = "10.18653/v1/2023.acl-long.787",
    pages = "14081--14094",
    abstract = "Current neural semantic parsers take a supervised approach requiring a considerable amount of training data which is expensive and difficult to obtain. Thus, minimizing the supervision effort is one of the key challenges in semantic parsing. In this paper, we propose the Retrieval as Ambiguous Supervision framework, in which we construct a retrieval system based on pretrained language models to collect high-coverage candidates. Assuming candidates always contain the correct ones, we convert zero-shot task into ambiguously supervised task. To improve the precision and coverage of such ambiguous supervision, we propose a confidence-driven self-training algorithm, in which a semantic parser is learned and exploited to disambiguate the candidates iteratively. Experimental results show that our approach significantly outperforms the state-of-the-art zero-shot semantic parsing methods.",
}

Current neural semantic parsers take a supervised approach requiring a considerable amount of training data which is expensive and difficult to obtain. Thus, minimizing the supervision effort is one of the key challenges in semantic parsing. In this paper, we propose the Retrieval as Ambiguous Supervision framework, in which we construct a retrieval system based on pretrained language models to collect high-coverage candidates. Assuming candidates always contain the correct ones, we convert zero-shot task into ambiguously supervised task. To improve the precision and coverage of such ambiguous supervision, we propose a confidence-driven self-training algorithm, in which a semantic parser is learned and exploited to disambiguate the candidates iteratively. Experimental results show that our approach significantly outperforms the state-of-the-art zero-shot semantic parsing methods.
Wu, Shan Xin, Chunlei Lin, Hongyu Han, Xianpei Liu, Cao Chen, Jiansong Yang, Fan Wan, Guanglu Sun, Le
Ambiguous Learning from Retrieval: Towards Zero-shot Semantic Parsing