  Multiple-Choice Question Answering (MCQA) is a challenging task in machine
reading comprehension. The main challenge in MCQA is to extract "evidence" from
the given context that supports the correct answer. In the OpenbookQA dataset,
the requirement of extracting "evidence" is particularly important due to the
mutual independence of sentences in the context. Existing work tackles this
problem by annotated evidence or distant supervision with rules which overly
rely on human efforts. To address the challenge, we propose a simple yet
effective approach termed evidence filtering to model the relationships between
the encoded contexts with respect to different options collectively and to
potentially highlight the evidence sentences and filter out unrelated
sentences. In addition to the effective reduction of human efforts of our
approach compared, through extensive experiments on OpenbookQA, we show that
the proposed approach outperforms the models that use the same backbone and
more training data; and our parameter analysis also demonstrates the
interpretability of our approach.
