论文标题
蒙特卡洛树搜索自然语言解释压力
Monte Carlo Tree Search for Interpreting Stress in Natural Language
论文作者
论文摘要
自然语言处理可以促进他们所写的文本对一个人的精神状态的分析。先前的研究开发了可以预测一个人是否以高准确性从社交媒体帖子中体验精神健康状况的模型。但是，这些模型无法解释为什么该人会经历特定的精神状态。在这项工作中，我们提出了一种使用Monte Carlo Tree Search（MCT）从文本中解释一个人的精神状态的新方法。我们的MCT算法采用训练有素的分类模型来指导搜索关键短语，这些短语以简洁，可解释的方式解释作者的精神状态。此外，我们的算法可以找到取决于文本的特定上下文（例如，最近的分解）和与上下文无关的解释。使用表现出压力的Reddit帖子的数据集，我们证明了MCTS算法在与上下文无关和上下文无关的方式中识别出对人压力感的可解释解释的能力。
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.
