YNU - HPCC at S em E val-2024 Task 2: Applying D e BERT a-v3-large to Safe Biomedical Natural Language Inference for Clinical Trials (2024)

Abstract: This paper describes the system for the YNU-HPCC team for SemEval2024 Task 2, focusing on Safe Biomedical Natural Language Inference for Clinical Trials. The core challenge of this task lies in discerning the textual entailment relationship between Clinical Trial Reports (CTR) and statements annotated by expert annotators, including the necessity to infer the relationships in texts subjected to semantic interventions accurately. Our approach leverages a fine-tuned DeBERTa-v3-large model augmented with supervised contrastive learning and back-translation techniques. Supervised contrastive learning aims to bolster classification ac-curacy while back-translation enriches the diversity and quality of our training corpus. Our method achieves a decent F1 score. However, the results also indicate a need for further en-hancements in the system’s capacity for deep semantic comprehension, highlighting areas for future refinement. The code of this paper is available at:https://github.com/RGTnuw/RG_YNU-HPCC-at-Semeval2024-Task2.

## introduction clinical trials constitute a critical component of medical research, evaluating the safety and efficacy of new treatment methods, medications, or medical devices @xcite . a significant num-ber of clinical trial reports (ctrs) are generated throughout clinical trials. these reports typically encompass information on research design, patient demographics, treatment protocols, outcomes (such as response rates and side effects), and overall conclusions. such comprehensive and transparent reporting of trial results provides the scientific community and the public with valuable information, informing future research and clinical practice @xcite . however, the challenge is compounded by over 400,000 clinical trial reports (ctrs) and their rapidly accelerating primary trial: single arm of healthy postmenopausal women to have two breast mri (baseline.... ## system description this section will describe the architecture of the proposed model in detail, including the data loader and back translation, the pre-trained model deberta-v3-large, and supervised contrastive learning; the system model we proposed is shown in figure 2 . ## conclusion this paper introduces a system based on finetuning and pre-training deberta-v3-large for se-meval2024 task 2, targeting safe biomedical nli for clinical trials. achieving seventh out of 32 with an f1 of 0.77 showcases the effectiveness of multitask pre-training, supervised contrastive learning, and back-translation despite struggles with intervention data and deep semantic under-standing. issues include truncated evidence from extended clinical trial premises @xcite and insufficient model depth for causal reasoning. future research should enhance semantic comprehension and causal reasoning and refine contrastive learning to improve the handling complex data and interventions, aiming to overcome current limitations in safe biomedical nli.