@inproceedings{jiang-etal-2018-chengyu,
title = "Chengyu Cloze Test",
author = "Jiang, Zhiying and
Zhang, Boliang and
Huang, Lifu and
Ji, Heng",
editor = "Tetreault, Joel and
Burstein, Jill and
Kochmar, Ekaterina and
Leacock, Claudia and
Yannakoudakis, Helen",
booktitle = "Proceedings of the Thirteenth Workshop on Innovative Use of {NLP} for Building Educational Applications",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-0516",
doi = "10.18653/v1/W18-0516",
pages = "154--158",
abstract = "We present a neural recommendation model for Chengyu, which is a special type of Chinese idiom. Given a query, which is a sentence with an empty slot where the Chengyu is taken out, our model will recommend the best Chengyu candidate that best fits the slot context. The main challenge lies in that the literal meaning of a Chengyu is usually very different from it{'}s figurative meaning. We propose a new neural approach to leverage the definition of each Chengyu and incorporate it as background knowledge. Experiments on both Chengyu cloze test and coherence checking in college entrance exams show that our system achieves 89.5{\%} accuracy on cloze test and outperforms human subjects who attended competitive universities in China. We will make all of our data sets and resources publicly available as a new benchmark for research purposes.",
}
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<abstract>We present a neural recommendation model for Chengyu, which is a special type of Chinese idiom. Given a query, which is a sentence with an empty slot where the Chengyu is taken out, our model will recommend the best Chengyu candidate that best fits the slot context. The main challenge lies in that the literal meaning of a Chengyu is usually very different from it’s figurative meaning. We propose a new neural approach to leverage the definition of each Chengyu and incorporate it as background knowledge. Experiments on both Chengyu cloze test and coherence checking in college entrance exams show that our system achieves 89.5% accuracy on cloze test and outperforms human subjects who attended competitive universities in China. We will make all of our data sets and resources publicly available as a new benchmark for research purposes.</abstract>
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%0 Conference Proceedings
%T Chengyu Cloze Test
%A Jiang, Zhiying
%A Zhang, Boliang
%A Huang, Lifu
%A Ji, Heng
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F jiang-etal-2018-chengyu
%X We present a neural recommendation model for Chengyu, which is a special type of Chinese idiom. Given a query, which is a sentence with an empty slot where the Chengyu is taken out, our model will recommend the best Chengyu candidate that best fits the slot context. The main challenge lies in that the literal meaning of a Chengyu is usually very different from it’s figurative meaning. We propose a new neural approach to leverage the definition of each Chengyu and incorporate it as background knowledge. Experiments on both Chengyu cloze test and coherence checking in college entrance exams show that our system achieves 89.5% accuracy on cloze test and outperforms human subjects who attended competitive universities in China. We will make all of our data sets and resources publicly available as a new benchmark for research purposes.
%R 10.18653/v1/W18-0516
%U https://aclanthology.org/W18-0516
%U https://doi.org/10.18653/v1/W18-0516
%P 154-158
Markdown (Informal)
[Chengyu Cloze Test](https://aclanthology.org/W18-0516) (Jiang et al., BEA 2018)
ACL
- Zhiying Jiang, Boliang Zhang, Lifu Huang, and Heng Ji. 2018. Chengyu Cloze Test. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 154–158, New Orleans, Louisiana. Association for Computational Linguistics.