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    <abstract>Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection. In this paper, we propose a stack-based multi-layer attention model for seq2seq learning to better leverage structural linguistics information. In our method, two binary vectors are used to track the decoding stack in transition-based parsing, and multi-layer attention is introduced to capture multiple word dependencies in partial trees. We conduct experiments on PTB and CTB datasets, and the results show that our proposed model achieves state-of-the-art accuracy and significant improvement in labeled precision with respect to the baseline seq2seq model.</abstract>
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%0 Conference Proceedings
%T Stack-based Multi-layer Attention for Transition-based Dependency Parsing
%A Zhang, Zhirui
%A Liu, Shujie
%A Li, Mu
%A Zhou, Ming
%A Chen, Enhong
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zhang-etal-2017-stack
%X Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection. In this paper, we propose a stack-based multi-layer attention model for seq2seq learning to better leverage structural linguistics information. In our method, two binary vectors are used to track the decoding stack in transition-based parsing, and multi-layer attention is introduced to capture multiple word dependencies in partial trees. We conduct experiments on PTB and CTB datasets, and the results show that our proposed model achieves state-of-the-art accuracy and significant improvement in labeled precision with respect to the baseline seq2seq model.
%R 10.18653/v1/D17-1175
%U https://aclanthology.org/D17-1175
%U https://doi.org/10.18653/v1/D17-1175
%P 1677-1682

Markdown (Informal)

[Stack-based Multi-layer Attention for Transition-based Dependency Parsing](https://aclanthology.org/D17-1175) (Zhang et al., EMNLP 2017)

• Stack-based Multi-layer Attention for Transition-based Dependency Parsing (Zhang et al., EMNLP 2017)

ACL

• Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, and Enhong Chen. 2017. Stack-based Multi-layer Attention for Transition-based Dependency Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1677–1682, Copenhagen, Denmark. Association for Computational Linguistics.