Context Modeling with Evidence Filter for Multiple Choice Question Answering
This work addresses the problem of reducing human effort in evidence extraction for MCQA, though it is incremental as it builds on existing methods with a novel filtering technique.
The paper tackled the challenge of extracting evidence for multiple-choice question answering by proposing an evidence filtering approach that models relationships between context sentences and options, which outperformed models with the same backbone and more training data on the OpenbookQA dataset.
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.