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        <title>Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts</title>
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        <namePart type="given">Dheeraj</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
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            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <abstract>We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective. Our system is aimed at engaging language learners by generating multiple-choice questions which utilize specific inference steps over multiple sentences, namely coreference resolution and paraphrase detection. The system also generates correct answers and semantically-motivated phrase-level distractors as answer choices. Evaluation by human annotators indicates that our approach requires a larger number of inference steps, which necessitate deeper semantic understanding of texts than a traditional single-sentence approach.</abstract>
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%0 Conference Proceedings
%T Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts
%A Araki, Jun
%A Rajagopal, Dheeraj
%A Sankaranarayanan, Sreecharan
%A Holm, Susan
%A Yamakawa, Yukari
%A Mitamura, Teruko
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F araki-etal-2016-generating
%X We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective. Our system is aimed at engaging language learners by generating multiple-choice questions which utilize specific inference steps over multiple sentences, namely coreference resolution and paraphrase detection. The system also generates correct answers and semantically-motivated phrase-level distractors as answer choices. Evaluation by human annotators indicates that our approach requires a larger number of inference steps, which necessitate deeper semantic understanding of texts than a traditional single-sentence approach.
%U https://aclanthology.org/C16-1107
%P 1125-1136

Markdown (Informal)

[Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts](https://aclanthology.org/C16-1107) (Araki et al., COLING 2016)

• Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts (Araki et al., COLING 2016)

ACL

• Jun Araki, Dheeraj Rajagopal, Sreecharan Sankaranarayanan, Susan Holm, Yukari Yamakawa, and Teruko Mitamura. 2016. Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1125–1136, Osaka, Japan. The COLING 2016 Organizing Committee.