Analysing relevance of Discourse Structure for Improved Mental Health Estimation (2024)

Abstract: Automated depression estimation has received significant research attention in recent years as a result of its growing impact on the global community. Within the context of studies based on patient-therapist interview transcripts, most researchers treat the dyadic discourse as a sequence of unstructured sentences, thus ignoring the discourse structure within the learning process. In this paper we propose Multi-view architectures that divide the input transcript into patient and therapist views based on sentence type in an attempt to utilize symmetric discourse structure for improved model performance. Experiments on DAIC-WOZ dataset for binary classification task within depression estimation show advantages of Multi-view architecture over sequential input representations. Our model also outperforms the current state-of-the-art results and provide new SOTA performance on test set of DAIC-WOZ dataset.

## introduction in recent years, automated depression estimation has attracted significant research initiatives which is unsurprising given the widespread impact and heavy toll of depression. within the context of depression estimation based on text, two major categories of input exist: (1) social media posts (twitter and reddit) of self-declared patients and (2) clinical interviews between patients and therapist. detection of depression is a challenging problem with patient-therapist interviews being the common practice to analyse a patient's mental health within clinical setting. within such dialogues, therapists look for indicative symptoms such as loss of interest, sadness, exhaustion, sleeping and eating disorders, etc. within patient's responses and base their evaluation on this information. complementary to these interviews, different self-assessment screening tools have also been defined such as the personal health questionnaire depression scale, with phq-8 being considered a valid diagnosis and severity measure for depressive disorders @xcite . throughout the literature, different strategies have been proposed for automatic estimation of depression, which consists of inferring the screening tool score based on the interview transcript. multi-modal models combine inputs from different modalities @xcite @xcite . multi-task architectures simultaneously learn related tasks @xcite . gender-aware models explore the impact of gender on depression estimation @xcite . hierarchical models process transcripts at different granularity levels @xcite . attention models integrate external knowledge from mental health lexicons @xcite . feature-based solutions compute multiple multi-modal characteristics @xcite . graph-based systems aim to study complex structures within interview transcripts @xcite . symptom-based models treat depression estimation as an extension of the symptom prediction problem @xcite . domain specific language models are built @xcite and large language models are prefix-tuned to automate depression level estimation @xcite . despite this extensive list of research initiatives, ways to express the structure of an input transcript remains a relatively unexplored research direction. indeed, most related works treat the overall transcript as a sequence of sentences taking into account the information contained in therapist questions and patient responses. these models disregard interview structure and consider it to be an unstructured list of sentences, forcing the model to learn inter-dependencies within the discourse. in this paper we argue that discourse structure combined with sentence type can improve models learning ability by reducing the number of noisy transactions within the data. in order to validate our hy- depression severity data split train val. test no symptoms [0..4] 47 17 22 mild [5..9] 29 6 11 non-depressed total 76 23 33 moderate [10..14] 20 5 5 moderately severe [15..19] 7 6 7 severe [20..24] 4 1 2 depressed total 31 12 14 total 107 35 47 ## dataset for our experiments we use the distress analysis interview corpus -wizard of oz (daic-woz) dataset which is part of a larger corpus, the distress analysis interview corpus (daic) @xcite . the dataset contains clinical interviews aimed towards psychological evaluation of participants for detecting conditions such as anxiety, depression and post-traumatic stress disorder. these interviews were collected with the goal of developing a computer agent that interviews participants to identify verbal and non-verbal signs for mental illness @xcite . in particular, we use wizard-of-oz interviews from the dataset which were conducted by virtual agent ellie, controlled by a human interviewer from another room. these interviews have been transcribed and annotated for a variety of verbal and non-verbal features. along with the transcripts, the dataset also contains corresponding visual and audio features extracted from the interview recordings. depression severity is accessed based on phq-8 depression scale, and score of 10 is used as threshold to differentiate between depressed and non-depressed participants. the dataset is divided into training, development and test sets containing 107, 35 and 47 interviews respectively. the dataset is biased towards lower phq-8 scores with almost 70% data points belonging to negative class in case of binary classification (phq-8 @xmath0. refer ## methodology studies have shown that questions asked by the therapist during an interview contain relevant information and provide context to patient responses. although @xcite validate the importance of therapist questions for depression estimation, they represent the input as an unstructured sequence of sentences. within this paper we emphasise on the importance of discourse structure for better understanding the input text. to take into account both patient and therapist information, while maintaining discourse symmetry and structure, we propose multi-view architecture that utilize sentence types to divide the interview into different views. our aim is to use this view based division of the transcript to control the number of noisy interactions, between unrelated questions and answers, learned by sequential models, allowing more efficient training of neural network models. ## experimental setup we use sentence-transformers @xcite , all-mpnet-base-v2 in particular, for generating sentence level text encodings used within our experiments. adam optimizer with weighted binary cross entropy loss (bceloss) is used during training to account for class imbalance in data. learning rate is treated as a hyperparameter and tuned during training. both encoders, global encoder and view encoder, are defined using transformer based multihead attention networks @xcite . cross-attention at view encoder level is also defined using multi-head attention mechanism with inputs from both views playing corresponding roles within query, key and value. various definitions of function f (α i , β i ) were experimented with and f was finally defined as a mean operation. pytorch framework is used for network definition and training of the models. ## results and analysis experiments were conducted on the daic-woz dataset @xcite and the best model is chosen based on macro f1 over the development set and evaluated based on performance on test set. during our experiments with different definitions of cross-attention function f (α, β), we observed that results obtained with non-balanced attention functions (i.e. only patient attention, only therapist attention, max) are lower compared to the balanced architectures (i.e. mean, learnable). within non-balanced functions, attention scores are transferred from one view to the other based on hypothesis that only one of the views drives the learning process. our results confirm that both views, questions and answers, are relevant, and selecting either one as the sole criteria for importance can be counterproductive. m ean function evidenced best performance within our experiments. or different modalities (svm:m-m&s @xcite ). note that the reported results are taken directly from the original papers, and that some related work surprisingly do not evidence results over the test split, such as hcag and hcag+t @xcite , although they highly perform on the development set. ## conclusion and future work in this paper, we propose a multi-view architecture for automated depression estimation that treats patient-therapist interviews as a combination of two views (therapist questions and patient answers). the underlying idea it to not only use inputs from both agents within the interview (patient and therapist), but also retain the inherent structure of the discourse for improved learning. in particular, the presented multi-view approach allows to handle discourse symmetry as well as discourse structure, thus outperforming the simple encoding of the input data as a sequence of sentences. results on the daic-woz show that the multi-view architecture steadily outperforms comparable baselines and evidences new state-of-the-art results. based on the insightful recent research of xezonaki et al. (xezonaki et al., 2020), we plan to further improve our results by incorporating external knowledge from different medical resources, such as lexicon or psychiatrist manual annotation.