YNU-HPCC at SemEval-2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data based on a BioBERT Model

被引：0

作者：

Feng, Chao ^{[1
]}

Wang, Jin ^{[1
]}

Zhang, Xuejie ^{[1
]}

机构：

[1] Yunnan Univ, Sch Informat Sci & Engn, Kunming, Yunnan, Peoples R China

来源：

17TH INTERNATIONAL WORKSHOP ON SEMANTIC EVALUATION, SEMEVAL-2023 | 2023年

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper describes the system for the YNU-HPCC team in subtask 1 of the SemEval2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). This task requires judging the textual entailment relationship between the given CTR and the statement annotated by the expert annotator. This system is based on the fine-tuned Bi-directional Encoder Representation from Transformers for Biomedical Text Mining (BioBERT) model with supervised contrastive learning and back translation. Supervised contrastive learning is to enhance the classification, and back translation is to enhance the training data. Our system achieved relatively good results on the competition's official leaderboard. The code of this paper is available at https://github.com/facanhe/SemEval-2023Task7.

引用

页码：664 / 670

页数：7

共 22 条

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Pahwa, Bhavika
17TH INTERNATIONAL WORKSHOP ON SEMANTIC EVALUATION, SEMEVAL-2023, 2023, : 1936 - 1944
[22] T5-Medical at SemEval-2024 Task 2: Using T5 Medical Embeddings for Natural Language Inference on Clinical Trial Data
Siino, Marco
PROCEEDINGS OF THE 18TH INTERNATIONAL WORKSHOP ON SEMANTIC EVALUATION, SEMEVAL-2024, 2024, : 40 - 46

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