@inproceedings{peng-etal-2017-addressing,
title = "Addressing the Data Sparsity Issue in Neural {AMR} Parsing",
author = "Peng, Xiaochang and
Wang, Chuan and
Gildea, Daniel and
Xue, Nianwen",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1035",
pages = "366--375",
abstract = "Neural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we describe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural attention model and our results are also competitive against state-of-the-art systems that do not use extra linguistic resources.",
}
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%0 Conference Proceedings
%T Addressing the Data Sparsity Issue in Neural AMR Parsing
%A Peng, Xiaochang
%A Wang, Chuan
%A Gildea, Daniel
%A Xue, Nianwen
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F peng-etal-2017-addressing
%X Neural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we describe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural attention model and our results are also competitive against state-of-the-art systems that do not use extra linguistic resources.
%U https://aclanthology.org/E17-1035
%P 366-375
Markdown (Informal)
[Addressing the Data Sparsity Issue in Neural AMR Parsing](https://aclanthology.org/E17-1035) (Peng et al., EACL 2017)
ACL
- Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. 2017. Addressing the Data Sparsity Issue in Neural AMR Parsing. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 366–375, Valencia, Spain. Association for Computational Linguistics.