M onte C arlo Tree Search for Interpreting Stress in Natural Language (2022)

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.

## introduction disabilities associated with mental health conditions pose a significant challenge for many people around the world @xcite @xcite . to help people suffering from these conditions, it is crucial to identify those who are experiencing a mental health condition and understand the underlying causes. natural language processing (nlp) can help by analyzing a person's mental state based on the text they have written. previous studies @xcite @xcite have demonstrated the ability of nlp models to process social media posts and predict stress, depression, and a range r/relationships: i can't believe this. my boyfriend just cheated on me and then he bragged about it on twitter. what kind of a messed up person would do that? i'm so angry with him and i'm sure we're going to have a huge fight about this when i see him tomorrow. figure 1: a fictitious example of text exhibiting stress in the relationships context and two explanations for that stress. the explanation in blue is context-dependent (specific to relationships) while the explanation in red is context-independent (general to any disagreement). in this work, we seek to explain the underlying causes of a person's mental state from their writing. we formulate such an explanation as a small set of phrases from the text that is sufficient to explain the person's mental state. we wish to identify two complementary types of explanations: those that are particular to the situation the person is in, which we call context-dependent, and those that could appear across different contexts, which we call context-independent. figure 1 shows an illustrating example. identifying both types of explanations not only enhances our understanding of the underlying sources of a person's mental state but also provides insights into how one's mental state can be affected by general and specific causes. to this end, we develop a novel monte carlo tree search (mcts) algorithm that can effectively identify explanations that are either context-dependent or context-independent by leveraging the semantic capabilities of trained nlp models. we, both quantitatively and qualitatively, demonstrate the efficacy of this approach to explain a person's mental state using a dataset of reddit posts that exhibit stress @xcite . ## the dreaddit dataset the dreaddit dataset @xcite ) contains 3,553 reddit posts that have human-annotated binary stress labels denoting whether a given text contains evidence of stress. each post belongs to one of ten subreddits (e.g., "r/relationships"), which we consider to be the con-text of the post. the posts are split into 2,838 train posts and 715 test posts. figures 8 and 9 (see appendix) show the distributions of the stress labels and subreddit categories for the train and test sets. ## method we assume that we have access to a training corpus d train and a test corpus d test to train and evaluate our models, respectively. the training corpus, d @xmath0, s i , c i ) @xmath1, is a set of tuples, where each tuple contains a text t @xmath2, • • • , t l i i } ∈ t consisting of l i tokens, its corresponding stress indicator s @xmath3, 1} denoting whether t i contains evidence of stress, and a context label c @xmath4. similarly, we assume ## experiments all of our experiments were run on the dreaddit dataset. we report results of our stress and context classification models and share findings of our mcts explanation algorithm. ## conclusion we propose a novel interpretability method for explaining stress in context-dependent and independent manners using monte carlo tree search. we demonstrate the effectiveness of our method by extracting both types of explanations from reddit posts that exhibit stress. although this work focuses on stress, our mcts-based explanation framework is extremely flexible and can be applied to a wide variety of nlp models and prediction problems simply by specifying the appropriate reward function and interpretability conditions for the search tree. as in our work, the reward function can include multiple objectives with different weights, making it possible to extract a variety of explanations for added interpretability. future work should further explore the range of explanations enabled by our framework. we hope that our explanation framework can improve understanding of the root causes of mental health conditions as well as provide interpretability for a variety of nlp tasks.