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        <title>Careful Selection of Knowledge to Solve Open Book Question Answering</title>
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        <namePart type="given">Pratyay</namePart>
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    <name type="personal">
        <namePart type="given">Chitta</namePart>
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    <abstract>Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.</abstract>
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%0 Conference Proceedings
%T Careful Selection of Knowledge to Solve Open Book Question Answering
%A Banerjee, Pratyay
%A Pal, Kuntal Kumar
%A Mitra, Arindam
%A Baral, Chitta
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F banerjee-etal-2019-careful
%X Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.
%R 10.18653/v1/P19-1615
%U https://aclanthology.org/P19-1615
%U https://doi.org/10.18653/v1/P19-1615
%P 6120-6129

Markdown (Informal)

[Careful Selection of Knowledge to Solve Open Book Question Answering](https://aclanthology.org/P19-1615) (Banerjee et al., ACL 2019)

• Careful Selection of Knowledge to Solve Open Book Question Answering (Banerjee et al., ACL 2019)

ACL

• Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra, and Chitta Baral. 2019. Careful Selection of Knowledge to Solve Open Book Question Answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6120–6129, Florence, Italy. Association for Computational Linguistics.