Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network Based on HowNet

Automatic evaluation of semantic rationality is an important yet challenging task, and current automatic techniques cannot effectively identify whether a sentence is semantically rational. Methods based on the language model do not measure the sentence by rationality but by commonness. 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 the 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 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.