Stochastic Answer Networks for Machine Reading Comprehension

Xiaodong LiuYelong ShenKevin DuhJianfeng Gao

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We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet which used reinforcement learning to determine the number of steps, the unique feature is the use of a kind of stochastic prediction dropout on the answer module (final layer) of the neural network during the training... (read more)

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