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This paper introduces a new neural structure called FusionNet, which extendsexisting attention approaches from three perspectives. First, it puts forward anovel concept of "history of word" to characterize attention information fromthe lowest word-level embedding up to the highest semantic-levelrepresentation. Second, it introduces an improved attention scoring functionthat better utilizes the "history of word" concept. Third, it proposes afully-aware multi-level attention mechanism to capture the complete informationin one text (such as a question) and exploit it in its counterpart (such ascontext or passage) layer by layer. We apply FusionNet to the Stanford QuestionAnswering Dataset (SQuAD) and it achieves the first position for both singleand ensemble model on the official SQuAD leaderboard at the time of writing(Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with twoadversarial SQuAD datasets and it sets up the new state-of-the-art on bothdatasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.

FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension