Stack-pointer networks for dependency parsing

Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig, and Eduard Hovy, in The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), 2018.

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Abstract

In this paper, we describe the details of the neural dependency parser sub-mitted by our team to the NLPCC 2019 Shared Task of Semi-supervised do-main adaptation subtask on Cross-domain Dependency Parsing. Our system is based on the stack-pointer networks(STACKPTR). Considering the im-portance of context, we utilize self-attention mechanism for the representa-tion vectors to capture the meaning of words. In addition, to adapt three dif-ferent domains, we utilize neural network based deep transfer learning which transfers the pre-trained partial network in the source domain to be a part of deep neural network in the three target domains (product comments, product blogs and web fiction) respectively. Results on the three target domains demonstrate that our model performs competitively.

Bib Entry

@inproceedings{ma2018stack,
  title = {Stack-pointer networks for dependency parsing},
  author = {Ma, Xuezhe and Hu, Zecong and Liu, Jingzhou and Peng, Nanyun and Neubig, Graham and Hovy, Eduard},
  booktitle = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)},
  volume = {1},
  year = {2018}
}