Junxiang Ren


2023

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CPIC at SemEval-2023 Task 7: GPT2-Based Model for Multi-evidence Natural Language Inference for Clinical Trial Data
Mingtong Huang | Junxiang Ren | Lang Liu | Ruilin Song | Wenbo Yin
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper describes our system submitted for SemEval Task 7, Multi-Evidence Natural Language Inference for Clinical Trial Data. The task consists of 2 subtasks. Subtask 1 is to determine the relationships between clinical trial data (CTR) and statements. Subtask 2 is to output a set of supporting facts extracted from the premises with the input of CTR premises and statements. Through experiments, we found that our GPT2-based pre-trained models can obtain good results in Subtask 2. Therefore, we use the GPT2-based pre-trained model to fine-tune Subtask 2. We transform the evidence retrieval task into a binary class task by combining premises and statements as input, and the output is whether the premises and statements match. We obtain a top-5 score in the evaluation phase of Subtask 2.