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        <title>A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension</title>
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        <namePart type="given">Seunghak</namePart>
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        <namePart type="given">Sathish</namePart>
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    <name type="personal">
        <namePart type="given">Haejun</namePart>
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    <abstract>Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.</abstract>
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    <identifier type="doi">10.18653/v1/W18-2603</identifier>
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%0 Conference Proceedings
%T A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension
%A Yu, Seunghak
%A Indurthi, Sathish Reddy
%A Back, Seohyun
%A Lee, Haejun
%S Proceedings of the Workshop on Machine Reading for Question Answering
%D 2018
%8 jul
%I Association for Computational Linguistics
%C Melbourne, Australia
%F yu-etal-2018-multi
%X Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.
%R 10.18653/v1/W18-2603
%U https://aclanthology.org/W18-2603
%U https://doi.org/10.18653/v1/W18-2603
%P 21-30

Markdown (Informal)

[A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension](https://aclanthology.org/W18-2603) (Yu et al., 2018)

• A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension (Yu et al., 2018)

ACL

• Seunghak Yu, Sathish Reddy Indurthi, Seohyun Back, and Haejun Lee. 2018. A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension. In Proceedings of the Workshop on Machine Reading for Question Answering, pages 21–30, Melbourne, Australia. Association for Computational Linguistics.