Machine comprehension question answering, which finds an answer to the
question given a passage, involves high-level reasoning processes of
understanding and tracking the relevant contents across various semantic units
such as words, phrases, and sentences in a document. This paper proposes the
novel question-aware sentence gating networks that directly incorporate the
sentence-level information into word-level encoding processes. To this end, our
model first learns question-aware sentence representations and then dynamically
combines them with word-level representations, resulting in semantically
meaningful word representations for QA tasks. Experimental results demonstrate
that our approach consistently improves the accuracy over existing baseline
approaches on various QA datasets and bears the wide applicability to other
neural network-based QA models.

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