  Automatic evaluation of semantic rationality is an important yet challenging
task, and current automatic techniques cannot well identify whether a sentence
is semantically rational. The methods based on the language model do not
measure the sentence by rationality but by commonness. The methods based on the
similarity with human written sentences will fail if human-written references
are not available. In this paper, we propose a novel model called
Sememe-Word-Matching Neural Network (SWM-NN) to tackle semantic rationality
evaluation by taking advantage of sememe knowledge base HowNet. The advantage
is that our model can utilize a proper combination of sememes to represent the
fine-grained semantic meanings of a word within the specific contexts. We use
the fine-grained semantic representation to help the model learn the semantic
dependency among words. To evaluate the effectiveness of the proposed model, we
build a large-scale rationality evaluation dataset. Experimental results on
this dataset show that the proposed model outperforms the competitive baselines
with a 5.4\% improvement in accuracy.
