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.

翻译：人类通过讨论、解释、相互认同或反对来共同解决问题。类似地，若系统在执行任务时能与人类进行讨论，则可提升系统的性能和可靠性。在以往的可解释性研究中，系统只能做出预测，人类只能就其提问，而无法进行双向意见交流。本研究旨在构建一个数据集和计算框架，使系统能够通过对话讨论并优化其预测。实验表明，所提出的系统能与人类进行有益的讨论，在自然语言推理任务中准确率提升高达25个百分点。