{"type":"ScholarlyArticle","title":"Domain Adaptation for Semantic Parsing","authors":[{"name":"Zechang Li","slug":"zechang-li","given":"Zechang","family":"Li","affiliation":"Wangxuan Institute of Computer Technology , Peking University , Beijing , China; Center for Data Science , Peking University , Beijing , China"},{"name":"Yuxuan Lai","slug":"yuxuan-lai","given":"Yuxuan","family":"Lai","affiliation":"Wangxuan Institute of Computer Technology , Peking University , Beijing , China"},{"name":"Yansong Feng","slug":"yansong-feng","given":"Yansong","family":"Feng","affiliation":"Wangxuan Institute of Computer Technology , Peking University , Beijing , China; The MOE Key Laboratory of Computational Linguistics , Peking University , China"},{"name":"Dongyan Zhao","slug":"dongyan-zhao","given":"Dongyan","family":"Zhao","affiliation":"Wangxuan Institute of Computer Technology , Peking University , Beijing , China; Center for Data Science , Peking University , Beijing , China"}],"abstract":"Recently, semantic parsing has attracted much attention in the community. Although many neural modeling efforts have greatly improved the performance, it still suffers from the data scarcity issue. In this paper, we propose a novel semantic parser for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain. Our semantic parser benefits from a two-stage coarse-to-fine framework, thus can provide different and accurate treatments for the two stages, i.e., focusing on domain invariant and domain specific information, respectively. In the coarse stage, our novel domain discrimination component and domain relevance attention encourage the model to learn transferable domain general structures. In the fine stage, the model is guided to concentrate on domain related details. Experiments on a benchmark dataset show that our method consistently outperforms several popular domain adaptation strategies.  Additionally, we show that our model can well exploit limited target data to capture the difference between the source and target domain, even when the target domain has far fewer training instances.","doi":"10.24963/ijcai.2020/515","url":"https://ijcai.org/proceedings/2020/515","pdf":"https://ijcai.org/proceedings/2020/0515.pdf","markdown":"https://ijcai.org/proceedings/2020/515.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/515","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Natural Language Processing","track":"Main track","number":515,"pages":{"first":3723,"last":3729},"keywords":["Natural Language Processing: Natural Language Semantics","Natural Language Processing: Natural Language Processing"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24471/file_24471.m3u8","length_sec":296,"provider":"hls"}],"references":[{"position":1,"raw":"[Bogin et al., 2019] Ben Bogin, Matt Gardner, and Jonathan Berant. 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