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        <title>Cut to the Chase: A Context Zoom-in Network for Reading Comprehension</title>
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        <namePart type="given">Sathish</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer. To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset ‘NarrativeQA’. The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement.</abstract>
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%0 Conference Proceedings
%T Cut to the Chase: A Context Zoom-in Network for Reading Comprehension
%A Indurthi, Sathish Reddy
%A Yu, Seunghak
%A Back, Seohyun
%A Cuayáhuitl, Heriberto
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F indurthi-etal-2018-cut
%X In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer. To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset ‘NarrativeQA’. The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement.
%R 10.18653/v1/D18-1054
%U https://aclanthology.org/D18-1054
%U https://doi.org/10.18653/v1/D18-1054
%P 570-575

Markdown (Informal)

[Cut to the Chase: A Context Zoom-in Network for Reading Comprehension](https://aclanthology.org/D18-1054) (Indurthi et al., EMNLP 2018)

• Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (Indurthi et al., EMNLP 2018)

ACL

• Sathish Reddy Indurthi, Seunghak Yu, Seohyun Back, and Heriberto Cuayáhuitl. 2018. Cut to the Chase: A Context Zoom-in Network for Reading Comprehension. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 570–575, Brussels, Belgium. Association for Computational Linguistics.