@inproceedings{wang-etal-2019-confusionset,
title = "Confusionset-guided Pointer Networks for {C}hinese Spelling Check",
author = "Wang, Dingmin and
Tay, Yi and
Zhong, Li",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1578",
doi = "10.18653/v1/P19-1578",
pages = "5780--5785",
abstract = "This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network, or generate a character from the confusionset rather than the entire vocabulary. We conduct experiments on three human-annotated datasets, and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20{\%} F1 score, achieving state-of-the-art performance on three datasets.",
}
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%0 Conference Proceedings
%T Confusionset-guided Pointer Networks for Chinese Spelling Check
%A Wang, Dingmin
%A Tay, Yi
%A Zhong, Li
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wang-etal-2019-confusionset
%X This paper proposes Confusionset-guided Pointer Networks for Chinese Spell Check (CSC) task. More concretely, our approach utilizes the off-the-shelf confusionset for guiding the character generation. To this end, our novel Seq2Seq model jointly learns to copy a correct character from an input sentence through a pointer network, or generate a character from the confusionset rather than the entire vocabulary. We conduct experiments on three human-annotated datasets, and results demonstrate that our proposed generative model outperforms all competitor models by a large margin of up to 20% F1 score, achieving state-of-the-art performance on three datasets.
%R 10.18653/v1/P19-1578
%U https://aclanthology.org/P19-1578
%U https://doi.org/10.18653/v1/P19-1578
%P 5780-5785
Markdown (Informal)
[Confusionset-guided Pointer Networks for Chinese Spelling Check](https://aclanthology.org/P19-1578) (Wang et al., ACL 2019)
ACL