ITNLP at SemEval-2021 Task 11: Boosting BERT with Sampling and Adversarial Training for Knowledge Extraction
Genyu Zhang, Yu Su, Changhong He, Lei Lin, Chengjie Sun, Lili Shan · 2021
This paper describes the winning system in the End-to-end Pipeline phase for the NLP-ContributionGraph task.The system is composed of three BERT-based models and the three models are used to extract sentences, phrases and triples respectively.Experiments show that sampling and adversarial training can greatly boost the system.In End-to-end Pipeline phase, our system got an average F1 of 0.4703, significantly higher than the secondplaced system which got an average F1 of 0.3828.