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Joint vision and language tasks like visual question answering arefascinating because they explore high-level understanding, but at the sametime, can be more prone to language biases. In this paper, we explore thebiases in the MovieQA dataset and propose a strikingly simple model which canexploit them. We find that using the right word embedding is of utmostimportance. By using an appropriately trained word embedding, about half theQuestion-Answers (QAs) can be answered by looking at the questions and answersalone, completely ignoring narrative context from video clips, subtitles, andmovie scripts. Compared to the best published papers on the leaderboard, oursimple question + answer only model improves accuracy by 5% for video +subtitle category, 5% for subtitle, 15% for DVS and 6% higher for scripts.

Are we asking the right questions in MovieQA?