Abstract
This paper introduces novel methodologies for the Natural Language Inference for Clinical Trials (NLI4CT) task. We present TLDR (T5-generated clinical-Language summaries for DeBERTa Report Analysis) which incorporates T5-model generated premise summaries for improved entailment and contradiction analysis in clinical NLI tasks. This approach overcomes the challenges posed by small context windows and lengthy premises, leading to a substantial improvement in Macro F1 scores: a 0.184 increase over truncated premises. Our comprehensive experimental evaluation, including detailed error analysis and ablations, confirms the superiority of TLDR in achieving consistency and faithfulness in predictions against semantically altered inputs.
Abstract (translated)
本文介绍了自然语言推理在临床试验（NLI4CT）任务中的新方法。我们提出了TLDR（T5生成的临床语言摘要）方法，该方法结合了T5模型生成的前提摘要，以提高临床NLI任务的准确性和矛盾分析。这种方法克服了小上下文窗口和长前提所带来的挑战，使得宏观F1得分提高了0.184：截短前提下的提高。我们的全面实验评估，包括详细的错误分析和消缺，证实了TLDR在实现对抗语义变换输入的预测一致性和可靠性方面具有优越性。
URL
https://arxiv.org/abs/2404.09136
