  Linguistically informed analyses of language models (LMs) contribute to the
understanding and improvement of these models. Here, we introduce the corpus of
Chinese linguistic minimal pairs (CLiMP), which can be used to investigate what
knowledge Chinese LMs acquire. CLiMP consists of sets of 1,000 minimal pairs
(MPs) for 16 syntactic contrasts in Mandarin, covering 9 major Mandarin
linguistic phenomena. The MPs are semi-automatically generated, and human
agreement with the labels in CLiMP is 95.8%. We evaluated 11 different LMs on
CLiMP, covering n-grams, LSTMs, and Chinese BERT. We find that classifier-noun
agreement and verb complement selection are the phenomena that models generally
perform best at. However, models struggle the most with the ba construction,
binding, and filler-gap dependencies. Overall, Chinese BERT achieves an 81.8%
average accuracy, while the performances of LSTMs and 5-grams are only
moderately above chance level.
