@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T An Improved Coarse-to-Fine Method for Solving Generation Tasks
%A Guan, Wenyv
%A Liu, Qianying
%A Han, Guangzhi
%A Wang, Bin
%A Li, Sujian
%Y Mistica, Meladel
%Y Piccardi, Massimo
%Y MacKinlay, Andrew
%S Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association
%D 2019
%8 4–6 dec
%I Australasian Language Technology Association
%C Sydney, Australia
%F guan-etal-2019-improved
%X 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.
%U https://aclanthology.org/U19-1024
%P 178-185
Markdown (Informal)
[An Improved Coarse-to-Fine Method for Solving Generation Tasks](https://aclanthology.org/U19-1024) (Guan et al., ALTA 2019)
ACL
- Wenyv Guan, Qianying Liu, Guangzhi Han, Bin Wang, and Sujian Li. 2019. An Improved Coarse-to-Fine Method for Solving Generation Tasks. In Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association, pages 178–185, Sydney, Australia. Australasian Language Technology Association.