The primary focus of recent work with large-scale transformers has been on optimizing the amount of information packed into the model's parameters. In this work, we ask a complementary question: Can multimodal transformers leverage explicit knowledge in their reasoning? Existing, primarily unimodal, methods have explored approaches under the paradigm of knowledge retrieval followed by answer prediction, but leave open questions about the quality and relevance of the retrieved knowledge used, and how the reasoning processes over implicit and explicit knowledge should be integrated. To address these challenges, we propose a - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result (+6\% absolute) on the open-domain multimodal task of OK-VQA. Our approach integrates implicit and explicit knowledge in an encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation. Additionally, explicit knowledge integration improves interpretability of model predictions in our analysis.

KAT: A Knowledge Augmented Transformer for Vision-and-Language

4D: Language Grounding to Vision 1

technical paper

## Welcome to NAACL 2022!
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Liangke Gui

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KAT: A Knowledge Augmented Transformer for Vision-and-Language

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