Abstract
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.
Cite
CITATION STYLE
Zhang, Y., Lin, J., Fan, Y., Jin, P., Liu, Y., & Liu, B. (2020). CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples. In 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings (pp. 494–500). International Committee for Computational Linguistics. https://doi.org/10.18653/v1/2020.semeval-1.60
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