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        <title>Visuo-Linguistic Question Answering (VLQA) Challenge</title>
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        <namePart type="given">Shailaja</namePart>
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    <name type="personal">
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            <namePart type="given">Trevor</namePart>
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    <abstract>Understanding images and text together is an important aspect of cognition and building advanced Artificial Intelligence (AI) systems. As a community, we have achieved good benchmarks over language and vision domains separately, however joint reasoning is still a challenge for state-of-the-art computer vision and natural language processing (NLP) systems. We propose a novel task to derive joint inference about a given image-text modality and compile the Visuo-Linguistic Question Answering (VLQA) challenge corpus in a question answering setting. Each dataset item consists of an image and a reading passage, where questions are designed to combine both visual and textual information i.e., ignoring either modality would make the question unanswerable. We first explore the best existing vision-language architectures to solve VLQA subsets and show that they are unable to reason well. We then develop a modular method with slightly better baseline performance, but it is still far behind human performance. We believe that VLQA will be a good benchmark for reasoning over a visuo-linguistic context. The dataset, code and leaderboard is available at https://shailaja183.github.io/vlqa/.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.413</identifier>
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%0 Conference Proceedings
%T Visuo-Linguistic Question Answering (VLQA) Challenge
%A Sampat, Shailaja Keyur
%A Yang, Yezhou
%A Baral, Chitta
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sampat-etal-2020-visuo
%X Understanding images and text together is an important aspect of cognition and building advanced Artificial Intelligence (AI) systems. As a community, we have achieved good benchmarks over language and vision domains separately, however joint reasoning is still a challenge for state-of-the-art computer vision and natural language processing (NLP) systems. We propose a novel task to derive joint inference about a given image-text modality and compile the Visuo-Linguistic Question Answering (VLQA) challenge corpus in a question answering setting. Each dataset item consists of an image and a reading passage, where questions are designed to combine both visual and textual information i.e., ignoring either modality would make the question unanswerable. We first explore the best existing vision-language architectures to solve VLQA subsets and show that they are unable to reason well. We then develop a modular method with slightly better baseline performance, but it is still far behind human performance. We believe that VLQA will be a good benchmark for reasoning over a visuo-linguistic context. The dataset, code and leaderboard is available at https://shailaja183.github.io/vlqa/.
%R 10.18653/v1/2020.findings-emnlp.413
%U https://aclanthology.org/2020.findings-emnlp.413
%U https://doi.org/10.18653/v1/2020.findings-emnlp.413
%P 4606-4616

Markdown (Informal)

[Visuo-Linguistic Question Answering (VLQA) Challenge](https://aclanthology.org/2020.findings-emnlp.413) (Sampat et al., Findings 2020)

• Visuo-Linguistic Question Answering (VLQA) Challenge (Sampat et al., Findings 2020)

ACL

• Shailaja Keyur Sampat, Yezhou Yang, and Chitta Baral. 2020. Visuo-Linguistic Question Answering (VLQA) Challenge. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4606–4616, Online. Association for Computational Linguistics.