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        <title>Monte Carlo Tree Search for Interpreting Stress in Natural Language</title>
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        <namePart type="given">Kyle</namePart>
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            <title>Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion</title>
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        <name type="personal">
            <namePart type="given">Bharathi</namePart>
            <namePart type="given">Raja</namePart>
            <namePart type="family">Chakravarthi</namePart>
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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.</abstract>
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%0 Conference Proceedings
%T Monte Carlo Tree Search for Interpreting Stress in Natural Language
%A Swanson, Kyle
%A Hsu, Joy
%A Suzgun, Mirac
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F swanson-etal-2022-monte
%X 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.
%R 10.18653/v1/2022.ltedi-1.12
%U https://aclanthology.org/2022.ltedi-1.12
%U https://doi.org/10.18653/v1/2022.ltedi-1.12
%P 107-119

Markdown (Informal)

[Monte Carlo Tree Search for Interpreting Stress in Natural Language](https://aclanthology.org/2022.ltedi-1.12) (Swanson et al., LTEDI 2022)

• Monte Carlo Tree Search for Interpreting Stress in Natural Language (Swanson et al., LTEDI 2022)

ACL

• Kyle Swanson, Joy Hsu, and Mirac Suzgun. 2022. Monte Carlo Tree Search for Interpreting Stress in Natural Language. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 107–119, Dublin, Ireland. Association for Computational Linguistics.