  Recently, much Chinese text error correction work has focused on Chinese
Spelling Check (CSC) and Chinese Grammatical Error Diagnosis (CGED). In
contrast, little attention has been paid to the complicated problem of Chinese
Semantic Error Diagnosis (CSED), which lacks relevant datasets. The study of
semantic errors is important because they are very common and may lead to
syntactic irregularities or even problems of comprehension. To investigate
this, we build the CSED corpus, which includes two datasets. The one is for the
CSED-Recognition (CSED-R) task. The other is for the CSED-Correction (CSED-C)
task. Our annotation guarantees high-quality data through quality assurance
mechanisms. Our experiments show that powerful pre-trained models perform
poorly on this corpus. We also find that the CSED task is challenging, as
evidenced by the fact that even humans receive a low score. This paper proposes
syntax-aware models to specifically adapt to the CSED task. The experimental
results show that the introduction of the syntax-aware approach is meaningful.
