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        <title>Faithful Multimodal Explanation for Visual Question Answering</title>
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            <namePart type="given">Tal</namePart>
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    <abstract>AI systems’ ability to explain their reasoning is critical to their utility and trustworthiness. Deep neural networks have enabled significant progress on many challenging problems such as visual question answering (VQA). However, most of them are opaque black boxes with limited explanatory capability. This paper presents a novel approach to developing a high-performing VQA system that can elucidate its answers with integrated textual and visual explanations that faithfully reflect important aspects of its underlying reasoning while capturing the style of comprehensible human explanations. Extensive experimental evaluation demonstrates the advantages of this approach compared to competing methods using both automated metrics and human evaluation.</abstract>
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    <identifier type="doi">10.18653/v1/W19-4812</identifier>
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%0 Conference Proceedings
%T Faithful Multimodal Explanation for Visual Question Answering
%A Wu, Jialin
%A Mooney, Raymond
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Belinkov, Yonatan
%Y Hupkes, Dieuwke
%S Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F wu-mooney-2019-faithful
%X AI systems’ ability to explain their reasoning is critical to their utility and trustworthiness. Deep neural networks have enabled significant progress on many challenging problems such as visual question answering (VQA). However, most of them are opaque black boxes with limited explanatory capability. This paper presents a novel approach to developing a high-performing VQA system that can elucidate its answers with integrated textual and visual explanations that faithfully reflect important aspects of its underlying reasoning while capturing the style of comprehensible human explanations. Extensive experimental evaluation demonstrates the advantages of this approach compared to competing methods using both automated metrics and human evaluation.
%R 10.18653/v1/W19-4812
%U https://aclanthology.org/W19-4812
%U https://doi.org/10.18653/v1/W19-4812
%P 103-112

Markdown (Informal)

[Faithful Multimodal Explanation for Visual Question Answering](https://aclanthology.org/W19-4812) (Wu & Mooney, BlackboxNLP 2019)

• Faithful Multimodal Explanation for Visual Question Answering (Wu & Mooney, BlackboxNLP 2019)

ACL

• Jialin Wu and Raymond Mooney. 2019. Faithful Multimodal Explanation for Visual Question Answering. In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 103–112, Florence, Italy. Association for Computational Linguistics.