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Deep models that are both effective and explainable are desirable in manysettings; prior explainable models have been unimodal, offering eitherimage-based visualization of attention weights or text-based generation ofpost-hoc justifications. We propose a multimodal approach to explanation, andargue that the two modalities provide complementary explanatory strengths. Wecollect two new datasets to define and evaluate this task, and propose a novelmodel which can provide joint textual rationale generation and attentionvisualization. Our datasets define visual and textual justifications of aclassification decision for activity recognition tasks (ACT-X) and for visualquestion answering tasks (VQA-X). We quantitatively show that training with thetextual explanations not only yields better textual justification models, butalso better localizes the evidence that supports the decision. We alsoqualitatively show cases where visual explanation is more insightful thantextual explanation, and vice versa, supporting our thesis that multimodalexplanation models offer significant benefits over unimodal approaches.

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence