A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony (2022)

Abstract: Mental Health Disorders continue plaguing humans worldwide. Aggravating this situation is the severe shortage of qualified and competent mental health professionals (MHPs), which underlines the need for developing Virtual Assistants (VAs) that can assist MHPs. The data+ML for automation can come from platforms that allow visiting and posting messages in peer-to-peer anonymous manner for sharing their experiences (frequently stigmatized) and seeking support. In this paper, we propose a VA that can act as the first point of contact and comfort for mental health patients. We curate a dataset, Motivational VA: MotiVAte comprising of 7k dyadic conversations collected from a peer-to-peer support platform. The system employs two mechanisms: (i) Mental Illness Classification: an attention based BERT classifier that outputs the mental disorder category out of the 4 categories, viz., Major Depressive Disorder (MDD), Anxiety, Obsessive Compulsive Disorder (OCD) and Post-traumatic Stress Disorder (PTSD), based on the input ongoing dialog between the support seeker and the VA; and (ii) Mental Illness Conditioned Motivational Dialogue Generation (MI-MDG): a sentiment driven Reinforcement Learning (RL) based motivational response generator. The empirical evaluation demonstrates the system capability by way of outperforming several baselines.

## introduction with an estimated 970 million individuals suffering from some sort of mental or neural diseases, mental health disorders are regarded one of the primary causes of disability globally foot_0 . poor access, stigma, and prejudice, on the other hand, are likely to limit clinical care to only 15% of individuals who are affected. as a means of expressing their emotions and experiences (generally stigmatized), millions of people (also known as support seekers) frequently turn to looking for emotional or mental health-related support @xcite on a variety of text-based peer-to-peer support platforms @xcite , (talklife.co). while peer supports on these platforms are wellintentioned and willing to aid and help seekers, they are often untrained and unaware of best-practices in therapy, resulting in wasted opportunities to provide effective and mutually engaging solutions @xcite . as a result, developing human-computer interfaces in the form of virtual assistants (vas) that can effectively reply and provide support to online support seekers becomes even more critical. empathy, or empathetic interactions @xcite , has been studied extensively in recent years @xcite as one of the most important aspects in providing successful support and triggering beneficial results in support-based dialogues. in addition to empathy, imparting hope and motivation (the process of thinking about and the willingness to move towards one's goals) have been recognised as important affective elements @xcite in uplifting the spirits of support seekers in distress during supportive talks. this is critical because support seekers often engage in escapist or avoidant behavior in anticipation of negative consequences, making it difficult for them to cope with the crisis @xcite . quantitative research shows that instilling optimistic behaviour fueled by hope and motivation improves symptoms in terms of positive psychological transformation and a favourable alliance in mental health support @xcite . in this paper, we propose a va acting as the first point of contact for mentally distressed support seekers afflicted with some form of mental illness. the va's efforts are aimed at reassuring and allowing support seekers to anonymously communicate and express their thoughts, emotions, challenges and seek support. the va's response should be competent and proficient enough to provide support seeker with a natural human experience focused on imparting hope and motivation based on positive perspective. to mimic this human-like behavior of mental health supporters in a va is quite challenging and the tasks employed are two folds. firstly, for the va to provide a conversational support in the absence of electronic health records or psychiatric notes, it is critical to recognise and differentiate various mental diseases because they are frequently communicated using similar language patterns and overall sentiment polarity. in the case of anxiety, for example, the supporter's purpose is to reduce avoidant behaviour and assist the patient in disconfirming a feared consequence. in depression, however, the goal is to assist the mental health seeker in experiencing positive feeling, a burst of energy, or another sort of pleasant contact with the world. subsequently, the task of the va is to generate response conditioned on the identified mental illness for modelling motivational conversations with positive outcome. due to the unavailability of conversational data for our proposed task, we introduce a dataset, moti-vate comprising of 7k conversations between support seekers and va collected from a peer-to-peer support platform. the key contributions of this paper are as follows @xcite to the best of our knowledge, this work is the first to propose a va for providing motivational support and comfort to mental health patients; (ii) we curate a conversational dataset, motivate, to advance research in mental health based support; (iii) our end-to-end system employs two sub-modules, viz., mental illness classification (mic) framework, a dual attention based bert classifier to identify the mental health disorder of the support seeker in the on-going conversation and mental illness conditioned motivational dialogue generation (mi-mdg) framework to generate mental illness conditioned sentiment driven reinforcement learning (rl) based motivational responses mimicing an ideal mental health supporter; (iv) empirical results indicate that our proposed system outperforms several baseline models. ## related works in this section, we explore mental health based analysis from social media posts and computational models for therapy @xcite . ## motivational va : motivate dataset the motivate dataset contains 7067 dyadic conversations with support seekers who have one of the four mental disorders: mdd, ptsd, anxiety, or ocd. supplementary material contains descriptions of these illnesses as they appear in icd-10. ## proposed methodology problem definition. the problem statement involves two parts : firstly, we aim to identify a textual on-going communication between a support seeker and the va as the conversation progresses in order to detect and differentiate mental health conditions. for a conversation t , given a seeker utterance, x @xmath0,1 , x t,2 , ..., x t,n ), a conversational context/history, @xmath1, c 2 , ..., c t-1 ), the task is to assign the most appropriate mental illness tag (say y 2 ) among a set of tags (@xmath2, y 2 , ...y i }, where i is the number of disorders considered). thus, it is a multi-class classification problem. formally, it can be represented as : @xmath3, c), where f is the developed classifier. the subsequent or the second part involves to solve the task of generating the next textual response of the va given the seeker utterance, its context of t -1 turns (say) and conditioned on the output y k (say) of the mental illness identification classifier. formally, given a seeker utterance, x @xmath4,1 , x t,2 , ..., x t,n ), a conversational context/history, @xmath5, c 2 , ..., c t-1 ), where c @xmath6, z i ) and mental illness category, y k , the task is to generate next textual response of the va, z @xmath7,1 , z t,2 , ..., z t,n ′′ ). summarization. while analyzing the dataset, we observed that the utterances in a dialogue have longer sequences implying longer context (also evident from ## experiments since the motivate dataset is imbalanced for different mental illness categories, we sample 50% of the dialogue from mdd subforum along with other categories to be utilized in the mic framework. thus, the mental illness classification module is trained on 5067 conversations, out of which 70% of the dialogues were used for training and remaining were utilized as test set. to encode different speaker utterances in the mic framework, a 300 dimensional bi-lstm layer was used. d f is a dense layer of 100 dimension. the four mental health categories are represented by 4 neurons in the output channels. in the final experiment, a learning rate of 0.01, textitcategorical crossentropy loss function, and adam optimizer were utilised. all of these parameters were chosen following a thorough sensitivity study. for training the mi-mdg framework, mic model is used as a pre-trained classifier providing additional input. for the mi-mdg model, we decode using default temperature and top-k values of the dialogpt model. adam optimizer is used to train the model. a learning rate of 0.00004 was found to be optimum. standard measures, such as the bleu-1 score @xcite , perplexity, rouge-l score @xcite what is now being discussed; and (iii) motivational : the response generated by the va should be positively-oriented imparting hope and motivation. finally, we report the average of the human rated scores across different users. ## results and analysis a series of experiments were carried out in order to evaluate the proposed framework. ## conclusion and future work online mental health support platforms that make use of peer supporters suffers from the biggest challenge of effectively training or scaffolding the peer supporters. in this research, we use ai to propose a virtual assistant (va) to provide support seekers with comfort and mental health support. as a first step, we created the motivate dataset, which contains dyadic conversations collected from a peerto-peer support network. we mold this system as a combination of two mechanism : (i) mental illness classification (mic) framework: a dual attention classifier that outputs the mental disorder category based on the ongoing dialog between the support seeker and the va; and (ii) mental illness conditioned motivational dialogue generation (mi-mdg) framework: a sentiment driven rl based motivational response generator conditioned on the mental state of the seeker. empirical results, both quantitative and qualitative validates the efficacy of the proposed approach. we surmise that this preliminary step will lead to promising direction for developing computational models to assist peer mental health support seekers and allow researchers to extend works on mental-health which is really the need of the hour. presents a number of ethical questions about user privacy that must be addressed @xcite . following the ethical guidelines established in previous research on various web-based platforms @xcite , we created our dataset using only publicly available discussions without using any personal profile information. before presenting the data to the annotators, we manually anonymized the profile and removed any disclosure of personal information (if any). despite the fact that the chats gathered from the online health forum were anonymized by their policy, the annotators pledged not to contact or deanonymize any of the users or share the data with others. this paper makes no therapy recommendations or clinical diagnostic claims. all the copyrights of the data belong to psychcentral.org. refer to the supplementary section for more details. we also acknowledge that in designing computational models for mental health support, there is a risk that responses trying to aid can have the opposite effect, which can be lethal resulting in self-harm. thus, risk mitigation steps are appropriate in this context. we stress on the fact that the system does not intend to make any clinical diagnosis or treatment of the disorder. it focuses on distinguishing mental state of the seekers based on semantic and linguistic evidence for the va to learn a generation policy. in such cases, even if the mental disorder is mis-classified, the va is focused on providing comfort and motivational support to the seekers. this is perfectly benign.