  Machine reading comprehension (MRC) is the task that asks a machine to answer
questions based on a given context. For Chinese MRC, due to the non-literal and
non-compositional semantic characteristics, Chinese idioms pose unique
challenges for machines to understand. Previous studies tend to treat idioms
separately without fully exploiting the relationship among them. In this paper,
we first define the concept of literal meaning coverage to measure the
consistency between semantics and literal meanings for Chinese idioms. With the
definition, we prove that the literal meanings of many idioms are far from
their semantics, and we also verify that the synonymic relationship can
mitigate this inconsistency, which would be beneficial for idiom comprehension.
Furthermore, to fully utilize the synonymic relationship, we propose the
synonym knowledge enhanced reader. Specifically, for each idiom, we first
construct a synonym graph according to the annotations from a high-quality
synonym dictionary or the cosine similarity between the pre-trained idiom
embeddings and then incorporate the graph attention network and gate mechanism
to encode the graph. Experimental results on ChID, a large-scale Chinese idiom
reading comprehension dataset, show that our model achieves state-of-the-art
performance.
