We present an accurate and interpretable method for answer extraction in
machine reading comprehension that is reminiscent of case-based reasoning (CBR)
from classical AI. Our method (CBR-MRC) builds on the hypothesis that
contextualized answers to similar questions share semantic similarities with
each other. Given a target question, CBR-MRC retrieves a set of similar
questions from a memory of observed cases and predicts an answer by selecting
the span in the target context that is most similar to the contextualized
representations of answers in the retrieved cases. The semi-parametric nature
of our approach allows CBR-MRC to attribute a prediction to the specific set of
cases used during inference, making it a desirable choice for building reliable
and debuggable QA systems. We show that CBR-MRC achieves high test accuracy
comparable with large reader models, outperforming baselines by 11.5 and 8.4 EM
on NaturalQuestions and NewsQA, respectively. Further, we also demonstrate the
ability of CBR-MRC in identifying not just the correct answer tokens but also
the span with the most relevant supporting evidence. Lastly, we observe that
contexts for certain question types show higher lexical diversity than others
and find CBR-MRC to be robust to these variations while performance using
fully-parametric methods drops.

提出了一种基于 CBR-MRC 的精确且可解释的答案提取方法，其利用相似问题之间的语义相似性来预测问题的答案，并在 NaturalQuestions 和 NewsQA 数据集上都取得了较高的准确率。