@inproceedings{shao-etal-2020-multi,
    title = "Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing",
    author = "Shao, Bo  and
      Gong, Yeyun  and
      Qi, Weizhen  and
      Duan, Nan  and
      Lin, Xiaola",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.coling-main.289/",
    doi = "10.18653/v1/2020.coling-main.289",
    pages = "3246--3256",
    abstract = "In this paper, we present a multi-level alignment pretraining method in a unified architecture formulti-lingual semantic parsing. In this architecture, we use an adversarial training method toalign the space of different languages and use sentence level and word level parallel corpus assupervision information to align the semantic of different languages. Finally, we jointly train themulti-level alignment and semantic parsing tasks. We conduct experiments on a publicly avail-able multi-lingual semantic parsing dataset ATIS and a newly constructed dataset. Experimentalresults show that our model outperforms state-of-the-art methods on both datasets."
}