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        <title>Multimodal Differential Network for Visual Question Generation</title>
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        <namePart type="given">Badri</namePart>
        <namePart type="given">Narayana</namePart>
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    <name type="personal">
        <namePart type="given">Sandeep</namePart>
        <namePart type="family">Kumar</namePart>
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    <name type="personal">
        <namePart type="given">Vinay</namePart>
        <namePart type="family">Namboodiri</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).</abstract>
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    <identifier type="doi">10.18653/v1/D18-1434</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Multimodal Differential Network for Visual Question Generation
%A Patro, Badri Narayana
%A Kumar, Sandeep
%A Kurmi, Vinod Kumar
%A Namboodiri, Vinay
%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 patro-etal-2018-multimodal
%X Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).
%R 10.18653/v1/D18-1434
%U https://aclanthology.org/D18-1434
%U https://doi.org/10.18653/v1/D18-1434
%P 4002-4012

Markdown (Informal)

[Multimodal Differential Network for Visual Question Generation](https://aclanthology.org/D18-1434) (Patro et al., EMNLP 2018)

• Multimodal Differential Network for Visual Question Generation (Patro et al., EMNLP 2018)

ACL

• Badri Narayana Patro, Sandeep Kumar, Vinod Kumar Kurmi, and Vinay Namboodiri. 2018. Multimodal Differential Network for Visual Question Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4002–4012, Brussels, Belgium. Association for Computational Linguistics.