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        <title>Transition-based Neural RST Parsing with Implicit Syntax Features</title>
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    <abstract>Syntax has been a useful source of information for statistical RST discourse parsing. Under the neural setting, a common approach integrates syntax by a recursive neural network (RNN), requiring discrete output trees produced by a supervised syntax parser. In this paper, we propose an implicit syntax feature extraction approach, using hidden-layer vectors extracted from a neural syntax parser. In addition, we propose a simple transition-based model as the baseline, further enhancing it with dynamic oracle. Experiments on the standard dataset show that our baseline model with dynamic oracle is highly competitive. When implicit syntax features are integrated, we are able to obtain further improvements, better than using explicit Tree-RNN.</abstract>
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%0 Conference Proceedings
%T Transition-based Neural RST Parsing with Implicit Syntax Features
%A Yu, Nan
%A Zhang, Meishan
%A Fu, Guohong
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F yu-etal-2018-transition
%X Syntax has been a useful source of information for statistical RST discourse parsing. Under the neural setting, a common approach integrates syntax by a recursive neural network (RNN), requiring discrete output trees produced by a supervised syntax parser. In this paper, we propose an implicit syntax feature extraction approach, using hidden-layer vectors extracted from a neural syntax parser. In addition, we propose a simple transition-based model as the baseline, further enhancing it with dynamic oracle. Experiments on the standard dataset show that our baseline model with dynamic oracle is highly competitive. When implicit syntax features are integrated, we are able to obtain further improvements, better than using explicit Tree-RNN.
%U https://aclanthology.org/C18-1047
%P 559-570

Markdown (Informal)

[Transition-based Neural RST Parsing with Implicit Syntax Features](https://aclanthology.org/C18-1047) (Yu et al., COLING 2018)

• Transition-based Neural RST Parsing with Implicit Syntax Features (Yu et al., COLING 2018)

ACL

• Nan Yu, Meishan Zhang, and Guohong Fu. 2018. Transition-based Neural RST Parsing with Implicit Syntax Features. In Proceedings of the 27th International Conference on Computational Linguistics, pages 559–570, Santa Fe, New Mexico, USA. Association for Computational Linguistics.