A Neural Network Based Translation Constrained Reranking Model for Chinese Dependency Parsing

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9427)

Abstract

Bilingual dependency parsing aims to improve parsing performance with the help of bilingual information. While previous work have shown improvements on either or both sides, most of them mainly focus on designing complicated features and rely on golden translations during training and testing. In this paper, we propose a simple yet effective translation constrained reranking model to improve Chinese dependency parsing. The reranking model is trained using a max-margin neural network without any manually designed features. Instead of using golden translations for training and testing, we relax the restrictions and use sentences generated by a machine translation system, which dramatically extends the scope of our model. Experiments on the translated portion of the Chinese Treebank show that our method outperforms the state-of-the-art monolingual Graph/Transition-based parsers by a large margin (UAS).

Keywords

Bilingual dependency parsing Reranking Neural network Machine translation 

Notes

Acknowledgments

This work is supported by National Key Basic Research Program of China (2014CB340504) and National Natural Science Foundation of China (61273318).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Key Laboratory of Computational Linguistics, Ministry of Education School of Electronics Engineering and Computer SciencePeking University Collaborative Innovation Center for Language AbilityXuzhouChina

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