Natural language processing can facilitate the analysis of a person's mental state from text they have written. Previous studies have developed models that can predict whether a person is experiencing a mental health condition from social media posts with high accuracy. Yet, these models cannot explain why the person is experiencing a particular mental state. In this work, we present a new method for explaining a person's mental state from text using Monte Carlo tree search (MCTS). Our MCTS algorithm employs trained classification models to guide the search for key phrases that explain the writer's mental state in a concise, interpretable manner. Furthermore, our algorithm can find both explanations that depend on the particular context of the text (e.g., a recent breakup) and those that are context-independent. Using a dataset of Reddit posts that exhibit stress, we demonstrate the ability of our MCTS algorithm to identify interpretable explanations for a person's feeling of stress in both a context-dependent and context-independent manner.

本文介绍了一种基于Monte Carlo Tree Search（MCTS）的解释性自然语言处理方法，采用训练好的分类模型来引导搜索能够简洁解释作者情绪的关键短语，该算法能够找到基于文本特定上下文和通用的解释，使用Reddit上的压力贴文数据集证明了该算法能够解释人们压力情绪的原因。

蒙特卡罗树搜索在自然语言中解释压力