CN-HIT-IT.NLP at SemEval-2020 Task 4: Enhanced Language Representation with Multiple Knowledge Triples

Yice Zhang, Jiaxuan Lin, Yang Fan, Peng Jin, Yuanchao Liu, Bingquan Liu


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.
Anthology ID:
2020.semeval-1.60
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
*SEMEVAL | COLING
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
494–500
Language:
URL:
https://www.aclweb.org/anthology/2020.semeval-1.60
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.semeval-1.60.pdf