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We present MCQA, a learning-based algorithm for multimodal questionanswering. MCQA explicitly fuses and aligns the multimodal input (i.e. text,audio, and video), which forms the context for the query (question and answer).Our approach fuses and aligns the question and the answer within this context.Moreover, we use the notion of co-attention to perform cross-modal alignmentand multimodal context-query alignment. Our context-query alignment modulematches the relevant parts of the multimodal context and the query with eachother and aligns them to improve the overall performance. We evaluate theperformance of MCQA on Social-IQ, a benchmark dataset for multimodal questionanswering. We compare the performance of our algorithm with prior methods andobserve an accuracy improvement of 4-7%.

MCQA: Multimodal Co-attention Based Network for Question Answering