SemEval-2023任务7:基于文本蕴意和证据检索的多粒度系统

Yuxuan Zhou, Ziyun Jin, Meiwei Li, Miao Li, Xien Liu, Xinxin You, Ji Wu
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引用次数: 1

摘要

NLI4CT任务的目的是根据临床试验报告(ctr)提出假设,并检索支持证明的相应证据。这项任务提出了一个重大挑战,因为验证NLI4CT任务中的假设需要整合来自一个或两个CTR的多个证据,并应用不同层次的推理,包括文本和数字。为了解决这些问题,本文提出了一种基于文本蕴涵和证据检索的多粒度系统。具体来说,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意和证据检索任务。此外,我们利用基于t5的模型SciFive增强了系统的数值推理能力,该模型在医学语料库上进行了预训练。系统进一步采用了模型集成和联合推理方法,提高了推理的稳定性和一致性。系统在文本蕴涵和证据检索任务上分别获得了0.856分和0.853分的f1分,在这两个子任务上都取得了最佳的性能。实验结果证实了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval
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.
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