The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification. This task poses a significant challenge, as verifying hypotheses in the NLI4CT task requires the integration of multiple pieces of evidence from one or two CTR(s) and the application of diverse levels of reasoning, including textual and numerical. To address these problems, we present a multi-granularity system for CTR-based textual entailment and evidence retrieval in this paper. Specifically, we construct a Multi-granularity Inference Network (MGNet) that exploits sentence-level and token-level encoding to handle both textual entailment and evidence retrieval tasks. Moreover, we enhance the numerical inference capability of the system by leveraging a T5-based model, SciFive, which is pre-trained on the medical corpus. Model ensembling and a joint inference method are further utilized in the system to increase the stability and consistency of inference. The system achieves f1-scores of 0.856 and 0.853 on textual entailment and evidence retrieval tasks, resulting in the best performance on both subtasks. The experimental results corroborate the effectiveness of our proposed method. Our code is publicly available at https://github.com/THUMLP/NLI4CT.

翻译：NLI4CT任务旨在基于临床试验报告（CTR）推断假设，并检索支持该论证的相应证据。该任务面临重大挑战，因为验证NLI4CT任务中的假设需要整合来自一份或两份CTR的多条证据，并应用包括文本推理与数值推理在内的多层级推理。为解决这些问题，本文提出了一种面向基于CTR的文本蕴含与证据检索的多粒度系统。具体而言，我们构建了一个多粒度推理网络（MGNet），该网络利用句子级和令牌级编码来处理文本蕴含与证据检索两项任务。此外，我们通过采用基于T5的模型SciFive（该模型已在医学语料上预训练），增强了系统的数值推理能力。系统中进一步采用了模型集成与联合推理方法，以提高推理的稳定性和一致性。该系统在文本蕴含与证据检索任务上分别取得了0.856和0.853的F1分数，在两个子任务上均实现了最佳性能。实验结果证实了我们所提出方法的有效性。我们的代码已在https://github.com/THUMLP/NLI4CT 公开。