@inproceedings{swanson-etal-2022-monte,
    title = "{M}onte {C}arlo Tree Search for Interpreting Stress in Natural Language",
    author = "Swanson, Kyle  and
      Hsu, Joy  and
      Suzgun, Mirac",
    editor = "Chakravarthi, Bharathi Raja  and
      Bharathi, B  and
      McCrae, John P  and
      Zarrouk, Manel  and
      Bali, Kalika  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.ltedi-1.12/",
    doi = "10.18653/v1/2022.ltedi-1.12",
    pages = "107--119",
    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."
}