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        <title>Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models</title>
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    <name type="personal">
        <namePart type="given">Mayank</namePart>
        <namePart type="family">Kulkarni</namePart>
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            <title>Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
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            <namePart type="given">Jill</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
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    <abstract>There has been an increase in popularity of data-driven question answering systems given their recent success. This pa-per explores the possibility of building a tutorial question answering system for Java programming from data sampled from a community-based question answering forum. This paper reports on the creation of a dataset that could support building such a tutorial question answering system and discusses the methodology to create the 106,386 question strong dataset. We investigate how retrieval-based and generative models perform on the given dataset. The work also investigates the usefulness of using hybrid approaches such as combining retrieval-based and generative models. The results indicate that building data-driven tutorial systems using community-based question answering forums holds significant promise.</abstract>
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    <identifier type="doi">10.18653/v1/W18-0532</identifier>
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%0 Conference Proceedings
%T Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models
%A Kulkarni, Mayank
%A Boyer, Kristy
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kulkarni-boyer-2018-toward
%X There has been an increase in popularity of data-driven question answering systems given their recent success. This pa-per explores the possibility of building a tutorial question answering system for Java programming from data sampled from a community-based question answering forum. This paper reports on the creation of a dataset that could support building such a tutorial question answering system and discusses the methodology to create the 106,386 question strong dataset. We investigate how retrieval-based and generative models perform on the given dataset. The work also investigates the usefulness of using hybrid approaches such as combining retrieval-based and generative models. The results indicate that building data-driven tutorial systems using community-based question answering forums holds significant promise.
%R 10.18653/v1/W18-0532
%U https://aclanthology.org/W18-0532
%U https://doi.org/10.18653/v1/W18-0532
%P 273-283

Markdown (Informal)

[Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models](https://aclanthology.org/W18-0532) (Kulkarni & Boyer, BEA 2018)

• Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models (Kulkarni & Boyer, BEA 2018)

ACL

• Mayank Kulkarni and Kristy Boyer. 2018. Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 273–283, New Orleans, Louisiana. Association for Computational Linguistics.