With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendations. To help automate this process and assist medical experts, NLP solutions are being developed. This motivated the SemEval-2023 Task 7, where the goal was to develop an NLP system for two tasks: evidence retrieval and natural language inference from clinical trial data. In this paper, we describe our two developed systems. The first one is a pipeline system that models the two tasks separately, while the second one is a joint system that learns the two tasks simultaneously with a shared representation and a multi-task learning approach. The final system combines their outputs in an ensemble system. We formalize the models, present their characteristics and challenges, and provide an analysis of achieved results. Our system ranked 3rd out of 40 participants with a final submission.
翻译:随着每天生成的临床试验报告数量增加,跟进最新发现以提供基于证据的医疗建议变得越来越困难。为了帮助自动化这一过程并协助医学专家,正在开发自然语言处理解决方案。这促进了SemEval-2023任务7的制定,目标是开发一个自然语言处理系统,用于两项任务:从临床试验数据中检索证据和执行自然语言推理。本文介绍了我们开发的两个系统。第一个是流水线系统,单独建模两个任务,而第二个是联合系统,采用共享表示和多任务学习方法同时学习两个任务。最终系统将它们的输出组合成一个集成系统。我们规范了模型,介绍了它们的特点和挑战,并提供了实现的结果分析。我们的系统在最终提交中排名第3,共有40个参与者。