Monte Carlo Tree Search for Interpreting Stress in Natural Language

TL;DR

This paper introduces a Monte Carlo tree search method to interpret and explain mental states from text, specifically identifying key phrases that reveal stress in social media posts, enhancing interpretability of NLP models.

Contribution

The paper presents a novel MCTS-based approach for explaining mental states from text, capable of identifying both context-dependent and independent explanations.

Findings

Successfully identified stress-related explanations in Reddit posts.

Demonstrated effectiveness in both context-dependent and independent scenarios.

Enhanced interpretability of mental health NLP models.

Abstract

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.