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Computer Science > Computation and Language

arXiv:2204.08105 (cs)
[Submitted on 17 Apr 2022]

Title:Monte Carlo Tree Search for Interpreting Stress in Natural Language

Authors:Kyle Swanson, Joy Hsu, Mirac Suzgun
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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.
Comments: Second Workshop on LT-EDI at ACL 2022
Subjects: Computation and Language (cs.CL); Information Theory (cs.IT)
Cite as: arXiv:2204.08105 [cs.CL]
  (or arXiv:2204.08105v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2204.08105

Submission history

From: Mirac Suzgun [view email]
[v1] Sun, 17 Apr 2022 23:06:01 UTC (1,880 KB)
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