通过人机协作解决自然语言处理问题：一种基于讨论的方法
Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach
AI总结：
针对以往NLP可解释性研究无法实现人机观点双向交互的问题，本研究构建了支持人机讨论优化预测的数据集与计算框架，实验显示该系统在自然语言推理任务中最高可提升25个百分点的准确率。
摘要：
人类通过开展讨论、作出解释以及表达赞同或反对来协作解决共同问题。同理，若系统在执行任务时能与人类展开讨论，便可提升自身性能与可靠性。在以往的可解释性研究中，仅能实现系统作出预测、人类针对预测提问，无法实现观点的双向交互。本研究旨在为通过对话讨论并优化预测结果的系统构建数据集与计算框架。实验表明，所提出的系统可与人类开展有益的讨论，在自然语言推理任务中将准确率提升最高25个百分点。
英文摘要：
Humans work together to solve common problems by having discussions, explaining, and agreeing or disagreeing with each other. Similarly, if a system can have discussions with humans when solving tasks, it can improve the system's performance and reliability. In previous research on explainability, it has only been possible for the system to make predictions and for humans to ask questions about them rather than having a mutual exchange of opinions. This research aims to create a dataset and computational framework for systems that discuss and refine their predictions through dialogue. Through experiments, we show that the proposed system can have beneficial discussions with humans improving the accuracy by up to 25 points in the natural language inference task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
