This paper describes our system that participated in the SemEval-2020 task 4: Commonsense Validation and Explanation. For this task, it is obvious that external knowledge, such as Knowledge graph, can help the model understand commonsense in natural language statements. But how to select the right triples for statements remains unsolved, so how to reduce the interference of irrelevant triples on model performance is a research focus. This paper adopt a modified K-BERT as the language encoder, to enhance language representation through triples from knowledge graphs. Experiments show that our method is better than models without external knowledge, and is slightly better than the original K-BERT. We got an accuracy score of 0.97 in subtaskA, ranking 1/45, and got an accuracy score of 0.948, ranking 2/35.

Cross-lingual topic detection within text is a feasible solution to resolving the language barrier in accessing the information. This paper presents a Chinese-English cross-lingual topic corpus (CLTC), in which 90,000 Chinese articles and 90,000 English articles are organized within 150 topics. Compared with TDT corpora, CLTC has three advantages. First, CLTC is bigger in size. This makes it possible to evaluate the large-scale cross-lingual text clustering methods. Second, articles are evenly distributed within the topics. Thus it can be used to produce test datasets for different purposes. Third, CLTC can be used as a cross-lingual comparable corpus to develop methods for cross-lingual information access. A preliminary evaluation with CLTC corpus indicates that the corpus is effective in evaluating cross-lingual topic detection methods.