  Current evaluation metrics to question answering based machine reading
comprehension (MRC) systems generally focus on the lexical overlap between the
candidate and reference answers, such as ROUGE and BLEU. However, bias may
appear when these metrics are used for specific question types, especially
questions inquiring yes-no opinions and entity lists. In this paper, we make
adaptations on the metrics to better correlate n-gram overlap with the human
judgment for answers to these two question types. Statistical analysis proves
the effectiveness of our approach. Our adaptations may provide positive
guidance for the development of real-scene MRC systems.
