Addressing the Data Sparsity Issue in Neural AMR Parsing

Xiaochang Peng; Chuan Wang; Daniel Gildea; Nianwen Xue

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arXiv · 1702.05053

Addressing the Data Sparsity Issue in Neural AMR Parsing

Xiaochang Peng·Chuan Wang·Daniel Gildea·Nianwen Xue

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Abstract

Neural attention models have achieved great success in different NLP tasks. How- ever, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we de- scribe 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 atten- tion model and our results are also compet- itive against state-of-the-art systems that do not use extra linguistic resources.

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Xiaochang Peng, Chuan Wang, Daniel Gildea, Nianwen Xue. 2017-02-16. Addressing the Data Sparsity Issue in Neural AMR Parsing. https://arxiv.org/abs/1702.05053

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