@inproceedings{guan-etal-2019-improved,
    title = "An Improved Coarse-to-Fine Method for Solving Generation Tasks",
    author = "Guan, Wenyv  and
      Liu, Qianying  and
      Han, Guangzhi  and
      Wang, Bin  and
      Li, Sujian",
    editor = "Mistica, Meladel  and
      Piccardi, Massimo  and
      MacKinlay, Andrew",
    booktitle = "Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association",
    month = "4--6 " # dec,
    year = "2019",
    address = "Sydney, Australia",
    publisher = "Australasian Language Technology Association",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/U19-1024/",
    pages = "178--185",
    abstract = "The coarse-to-fine (coarse2fine) methods have recently been widely used in the generation tasks. The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage. However, they usually lack the correction ability when getting a wrong sketch. To solve this problem, in this paper, we propose an improved coarse2fine model with a control mechanism, with which our method can control the influence of the sketch on the final results in the fine stage. Even if the sketch is wrong, our model still has the opportunity to get a correct result. We have experimented our model on the tasks of semantic parsing and math word problem solving. The results have shown the effectiveness of our proposed model."
}