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Several deep learning models have been proposed for question answering.However, due to their single-pass nature, they have no way to recover fromlocal maxima corresponding to incorrect answers. To address this problem, weintroduce the Dynamic Coattention Network (DCN) for question answering. The DCNfirst fuses co-dependent representations of the question and the document inorder to focus on relevant parts of both. Then a dynamic pointing decoderiterates over potential answer spans. This iterative procedure enables themodel to recover from initial local maxima corresponding to incorrect answers.On the Stanford question answering dataset, a single DCN model improves theprevious state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains80.4% F1.

Dynamic Coattention Networks For Question Answering