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        <title>Structural Embedding of Syntactic Trees for Machine Comprehension</title>
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        <namePart type="given">Rui</namePart>
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    <name type="personal">
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        <namePart type="family">Yang</namePart>
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    <abstract>Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.</abstract>
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%0 Conference Proceedings
%T Structural Embedding of Syntactic Trees for Machine Comprehension
%A Liu, Rui
%A Hu, Junjie
%A Wei, Wei
%A Yang, Zi
%A Nyberg, Eric
%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 liu-etal-2017-structural
%X Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods.
%R 10.18653/v1/D17-1085
%U https://aclanthology.org/D17-1085
%U https://doi.org/10.18653/v1/D17-1085
%P 815-824

Markdown (Informal)

[Structural Embedding of Syntactic Trees for Machine Comprehension](https://aclanthology.org/D17-1085) (Liu et al., EMNLP 2017)

• Structural Embedding of Syntactic Trees for Machine Comprehension (Liu et al., EMNLP 2017)

ACL

• Rui Liu, Junjie Hu, Wei Wei, Zi Yang, and Eric Nyberg. 2017. Structural Embedding of Syntactic Trees for Machine Comprehension. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 815–824, Copenhagen, Denmark. Association for Computational Linguistics.