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    <abstract>Reading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task. In this paper, we study the RC problem on the Stanford Question Answering Dataset (SQuAD). Observing from the training set that most correct answers are centered around constituents in the parse tree, we design a constituent-centric neural architecture where the generation of candidate answers and their representation learning are both based on constituents and guided by the parse tree. Under this architecture, the search space of candidate answers can be greatly reduced without sacrificing the coverage of correct answers and the syntactic, hierarchical and compositional structure among constituents can be well captured, which contributes to better representation learning of the candidate answers. On SQuAD, our method achieves the state of the art performance and the ablation study corroborates the effectiveness of individual modules.</abstract>
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%0 Conference Proceedings
%T A Constituent-Centric Neural Architecture for Reading Comprehension
%A Xie, Pengtao
%A Xing, Eric
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 jul
%I Association for Computational Linguistics
%C Vancouver, Canada
%F xie-xing-2017-constituent
%X Reading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task. In this paper, we study the RC problem on the Stanford Question Answering Dataset (SQuAD). Observing from the training set that most correct answers are centered around constituents in the parse tree, we design a constituent-centric neural architecture where the generation of candidate answers and their representation learning are both based on constituents and guided by the parse tree. Under this architecture, the search space of candidate answers can be greatly reduced without sacrificing the coverage of correct answers and the syntactic, hierarchical and compositional structure among constituents can be well captured, which contributes to better representation learning of the candidate answers. On SQuAD, our method achieves the state of the art performance and the ablation study corroborates the effectiveness of individual modules.
%R 10.18653/v1/P17-1129
%U https://aclanthology.org/P17-1129
%U https://doi.org/10.18653/v1/P17-1129
%P 1405-1414

Markdown (Informal)

[A Constituent-Centric Neural Architecture for Reading Comprehension](https://aclanthology.org/P17-1129) (Xie & Xing, ACL 2017)

• A Constituent-Centric Neural Architecture for Reading Comprehension (Xie & Xing, ACL 2017)

ACL

• Pengtao Xie and Eric Xing. 2017. A Constituent-Centric Neural Architecture for Reading Comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1405–1414, Vancouver, Canada. Association for Computational Linguistics.