Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues (2022)

Abstract: Conversational bots have become non-traditional methods for therapy among individuals suffering from psychological illnesses. Leveraging deep neural generative language models, we propose a deep trainable neural conversational model for therapy-oriented response generation. We leverage transfer learning methods during training on therapy and counseling based data from Reddit and AlexanderStreet. This was done to adapt existing generative models – GPT2 and DialoGPT – to the task of automated dialog generation. Through quantitative evaluation of the linguistic quality, we observe that the dialog generation model - DialoGPT (345M) with transfer learning on video data attains scores similar to a human response baseline. However, human evaluation of responses by conversational bots show mostly signs of generic advice or information sharing instead of therapeutic interaction.

## introduction psychological and mental disorders, such as depression and anxiety, are a growing concern worldwide. about an estimated 5% of the global adult population suffer from depression. 1 the national alliance on mental illness (nami) reports 1 in 5 adults in the u.s. were diagnosed with mental health issues. 2 psychological ailments are complicated and challenging to diagnose and can manifest in an individual, in any form, regardless of their age, race, and gender. in extreme situations, lack of diagnosis and proper treatment can also be fatal. 2 however, only a fraction of the suffering individuals seek proper treatment from a mental health profes1 institute of health metrics and evaluation -global health data exchange (ghdx), http://ghdx.healthdata.org/gbd-results-tool?@xmath0://www.nami.org/mhstats sional due to the existing stigma surrounding mental health. additionally, the growing dearth in the current clinical workforce also adds to the problem. this impending crisis has led to a growing interest in automated conversational bots as non-traditional methods of receiving treatment for mental health @xcite . a majority of the available conversational agents generate responses based on predefined rules or tree-based dialog flows, and may not be useful for therapeutic counseling @xcite due to shallow and ineffective conversations @xcite . recent developments in massive language modeling through deep learning has resulted in successful outcomes in natural language understanding and generation tasks. transformer architectures like openai's gpt-2 @xcite have been used in conversational modeling and dialog generation with great empirical success @xcite . while deep neural learning has helped improve the cognitive capability of these conversational agents, training such chatbots for a particular task require massive amounts of in-domain conversational data. currently available massive pre-trained models have been trained on a motley of webscraped articles and conversations on social media platforms @xcite that may contain toxic and aggressive content @xcite . therefore, dialog models pre-trained on such text can often generate responses that are harmful and callous, making them unsuitable for conversational psychotherapy @xcite . existing research on neural response generation has generated multiple data sets for evaluating dialog responses, however data related to mental health counseling is very limited @xcite . additionally, a majority of these data sets have been collected through crowd-sourced human-human conversa-tions @xcite , video transcripts of motivational interviewing @xcite , through text messaging @xcite , etc. mental health and psychological counseling data is sensitive with limited access and availabilityrestricting the improvement of dialog agents in the domain of psychotherapy counseling. this study leverages existing generative architectures like the dialogpt @xcite , an open-domain dialog model based on openai's gpt-2 @xcite , for therapeutic conversational modeling for mental and emotional support. we additionally explore model fine-tuning through transfer learning on therapy counseling videos for therapy-based response generation @xcite . for pre-training and fine-tuning, we use subreddit threads that contain submissions on therapy and counseling, mental disorders and ailments @xcite , and transcripts of english therapy and counseling videos from alexanderstreet website. selecting the top models through a metric-based quantitative evaluation @xcite , we perform a task-based effectiveness study through human evaluation setup. ## related work while conversational agents have been used for multiple reasons, one major area of application is the diagnosis and treatment of psychological illnesses. from the first simple conversational agent eliza developed in 1966 by joseph weizenbaum to act as a rogerian psychotherapist, current chatbots have undergone major improvements with the advancements in the field of artificial intelligence (ai) like natural language processing (nlp) and machine learning (ml) @xcite . ## data collection to train and incorporate the attributes of therapy in conversation -we collect the data by scraping subreddit threads on mental health and transcripts of videos on psychotherapy and counseling. we extract the conversations about mental health and therapy from online sources and platforms such as reddit @xcite and alexander street press. 3 ## results we discuss in detail the qualitative and quantitative evaluation metrics and performance of the different generative language models for therapy-based dialog response generation. we show our evaluation setup in figure 3 . we first perform a quanti-tative metric-based evaluation on an in-domain test dataset of therapy videos. the test dataset for model evaluation consists of 114 videos collected from alexanderstreet therapy and counseling training videos. the dataset has a total of 18,237 dialog utterances and 312,276 words. to compare the generated response with the test reference set, we select conversation length of at least 5 utterances as the maximum sequence length for the next utterance generation. given a sequence of n responses, the generated n + 1 th response is compared to the corresponding human response in the original conversation. here, we evaluate the model performance for a single utterance given a prompt. for calculating the human system metrics, we created a held-out subset of 50 conversations to calculate the metrics. these 50 conversations were selected from the alexanderstreet video test dataset. we calculate the metrics by using the n -th response from these conversations and comparing them with the human ground truth response. for the qualitative and task-based effectiveness analysis, we selected responses generated by the top three models from the quantitative evaluation, and showed them to our psychotherapy domain experts to judge. we further calculate the inter-annotator agreement between two judges to measure the effectiveness of the systems and their feasibility in automated therapy-based counseling. we present the scores of the automated quantitative metrics provided by the chateval tool @xcite on the video test dataset in ## conclusion we propose a novel technique to adapt existing open-domain pre-trained generative models, di-alogpt (dialog-based) and gpt-2, for therapeutic conversation modeling. to fine-tune the model to the specific task of didactic conversations, we use a transfer learning technique to model aspects of therapist-patient counseling extracted from therapy video transcripts. on existing automated quantitative evaluation metrics, dialogpt performed better than the gpt-2. furthermore, a task-based effectiveness evaluation by human judges of the generated samples show that the models, pre-trained on a subsequently larger content of subreddit data, generate responses with unhealthy advice or generic information usually discouraged in traditional therapeutic counseling. this indicates the need for further improvements in automated therapy counseling like domain-specific clinical and therapy data.