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We demonstrate the surprising strength of unimodal baselines in multimodal domains, and make concrete recommendations for best practices in future research. Where existing work often compares against random or majority class baselines, we argue that unimodal approaches better capture and reflect dataset biases and therefore provide an important comparison when assessing the performance of multimodal techniques. We present unimodal ablations on three recent datasets in visual navigation and QA,doi:10.18653/v1/n19-1197 dblp:conf/naacl/ThomasonGB19 fatcat:hpymijvtm5emfbxcbxwp6zjmti