Inferential Machine Comprehension: Answering Questions by
Recursively Deducing the Evidence Chain from Text
Abstract
This paper focuses on the topic of inferential
machine comprehension, which aims to fully understand the meanings of given text to
answer generic questions, especially the ones
needed reasoning skills. In particular, we first
encode the given document, question and options in a context aware way. We then propose
a new network to solve the inference problem
by decomposing it into a series of attentionbased reasoning steps. The result of the previous step acts as the context of next step. To
make each step can be directly inferred from
the text, we design an operational cell with prior structure. By recursively linking the cells,
the inferred results are synthesized together to
form the evidence chain for reasoning, where
the reasoning direction can be guided by imposing structural constraints to regulate interactions on the cells. Moreover, a termination
mechanism is introduced to dynamically determine the uncertain reasoning depth, and the
network is trained by reinforcement learning.
Experimental results on 3 popular data sets, including MCTest, RACE and MultiRC, demonstrate the effectiveness of our approach