@inproceedings{zhang-etal-2021-itnlp,
title = "{ITNLP} at {S}em{E}val-2021 Task 11: Boosting {BERT} with Sampling and Adversarial Training for Knowledge Extraction",
author = "Zhang, Genyu and
Su, Yu and
He, Changhong and
Lin, Lei and
Sun, Chengjie and
Shan, Lili",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.59/",
doi = "10.18653/v1/2021.semeval-1.59",
pages = "485--489",
abstract = "This paper describes the winning system in the End-to-end Pipeline phase for the NLPContributionGraph task. The system is composed of three BERT-based models and the three models are used to extract sentences, entities 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 second-placed system which got an average F1 of 0.3828."
}
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%0 Conference Proceedings
%T ITNLP at SemEval-2021 Task 11: Boosting BERT with Sampling and Adversarial Training for Knowledge Extraction
%A Zhang, Genyu
%A Su, Yu
%A He, Changhong
%A Lin, Lei
%A Sun, Chengjie
%A Shan, Lili
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2021-itnlp
%X This paper describes the winning system in the End-to-end Pipeline phase for the NLPContributionGraph task. The system is composed of three BERT-based models and the three models are used to extract sentences, entities 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 second-placed system which got an average F1 of 0.3828.
%R 10.18653/v1/2021.semeval-1.59
%U https://aclanthology.org/2021.semeval-1.59/
%U https://doi.org/10.18653/v1/2021.semeval-1.59
%P 485-489
Markdown (Informal)
[ITNLP at SemEval-2021 Task 11: Boosting BERT with Sampling and Adversarial Training for Knowledge Extraction](https://aclanthology.org/2021.semeval-1.59/) (Zhang et al., SemEval 2021)
ACL