Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on R eddit Posts (2022)

Abstract: Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of ‘depression’, our experiments show that DLMs coupled with process knowledge in a mental health questionnaire generate 12.54% and 9.37% better FQs based on similarity and longest common subsequence matches to questions in the PHQ-9 dataset respectively, when compared with DLMs without process knowledge support. Despite coupling with process knowledge, we find that DLMs are still prone to hallucination, i.e., generating redundant, irrelevant, and unsafe FQs. We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge. To address this limitation, we prepared an extended PHQ-9 based dataset, PRIMATE, in collaboration with MHPs. PRIMATE contains annotations regarding whether a particular question in the PHQ-9 dataset has already been answered in the user’s initial description of the mental health condition. We used PRIMATE to train a DLM in a supervised setting to identify which of the PHQ-9 questions can be answered directly from the user’s post and which ones would require more information from the user. Using performance analysis based on MCC scores, we show that PRIMATE is appropriate for identifying questions in PHQ-9 that could guide generative DLMs towards controlled FQ generation (with minimal hallucination) suitable for aiding triaging. The dataset created as a part of this research can be obtained from https://github.com/primate-mh/Primate2022

## introduction conversational agents (cas) powered by dlms are software designed to interact with human users for specific tasks. for mental health purposes, particularly depression, cas have been studied extensively in prior work for helping patients follow generic mental health guidelines, typically by providing reminders to assist patients in adhering to the medication and therapy strategy outlined by a mental health professional (mhp) 12 . however, previous work on depression have not examined the use of cas for triaging. for the purpose of triaging, cas should learn to generate controlled and clinical process knowledge-guided discourse that can assist mhps in diagnosis. our research suggests a clinically grounded and explainable methodology to develop conversational information-seeking tools, first to learn "what symptoms the user is suffering" and "what extra information is needed for triaging." cas are susceptible to irrelevant and sometimes harmful questions when generating fqs or responses to a patient suffering from depression @xcite . the primary reason for irrelevant and harmful questions is that cas cannot incorporate contextual information in generating appropriate follow-up questions (fqs) (see figure 1 ). further, the sensitivity of the conversation and a controlled generation process are essential characteristics of patient-clinician interactions, which are difficult to embed in dlm-based cas. therefore, question generation (qg) in mental health is challenging, and research to develop cas for automating triage has not been explored. figure 1 : reddit is a rich source for bringing crowd perspective in training dlms over conversational data. on the left is a sample post from r/depression help which sees inquisitive interaction from other reddit users. at the top-right are the fqs asked by the reddit users in the comments. these fqs are aimed at understanding the severity of the mental health situation of the user and are hence, diagnostically relevant. at the bottom-right are the questions generated by dlms. it can be seen that these are not suitable fqs. procedures for generating semantically related and logically ordered questions in the mental health domain are a form of process knowledge manifested in various clinical instruments for mental health triage. for example, the severity of depression is measured using patient health questionnaire (phq-9). enforcing dlms to follow process knowledge, like in phq-9, would make cas generate fqs similar to an mhp when they are seeking information from the patient @xcite . unfortunately, datasets that meet this criterion are currently unavailable. though clinical diagnostic interviews exist, they are not rich, sufficiently dense, and varied to train dlms @xcite . further, we require dataset(s) that includes support seeking queries and natural questions that show help providing behavior. for this purpose, anonymized usergenerated conversational data in mental health support communities on reddit provides a rich source of fine-grained, contextual, and diverse information suitable for fine-tuning dlms. specific to depression, we explored posts and comments in r/depression help. in the current research, we emphasize the limitations of t5, a state-of-the-art dlm 3 to generate process knowledge-like fqs using the data from 3 current dlms are either variants of t5 or built from t5 r/depression help @xcite . we filtered the dataset by retaining only posts with at least one comment that seeks additional information from the user seeking support. further filtering of comments was performed using phq-9 to assist t5 in generating relevant fqs (see figure 2 ). we found that the outcome is substantial for the single turn question answering model; however, not suitable for mental health triage, which is a discourse. we conducted a series of experiments keeping our focus on 'depression' and leveraged its associated process knowledge for mental health triage: the phq-9 @xcite . to the best of our knowledge, fq generation relating to depression has never been studied using phq-9 for discourse modeling and generation. we make the following key contributions: (a) extending phq-9: phq-9 questions are limited in scope for common nlp tasks like finetuning. in collaboration with mhps, we prepared a list of 134 sub-questions for nine phq-9 questions for better fine-tuning of t5. (b) we analyzed the performance of three variants of t5 using bleurt @xcite and rouge-l scores that measure semantic relatedness and exact match similarity of generated question to sub-questions of phq-9. (c) primate dataset: lessons learned during our experiments suggested that t5 must be trained in a supervised setting to capture 'what the user has already mentioned about his/her depression condition in the post-text' and then generate fqs. along with mhps, we constructed a novel primate (process knowledge integrated mental health dataset) dataset that would train dlms to capture phq-9-answerable information from user text. in this research, we restrict our experiments and discussion on whether primate can help capture context from the user post relevant to some phq-9 questions and pointing out which other phq-9 questions would form candidates to direct fq generation. our approach and insights have applications to anxiety (gad-7), suicide (c-ssrs), and other mental health disorders as well. ## related work recently, dlms have attracted much attention for question answering, thanks to their successes in nlp applications @xcite . research on question generation has focused on improving the legibility and relevance of questions. this is because dlms continue to hallucinate while generating questions in general-purpose domains, which can lead to factually incorrect responses. this can have severe consequences in the mental health domain @xcite . recently, inappropriate and toxic behaviors of language models have been extensively studied and reported in the literature @xcite . solutions around fine-tuning, augmenting a neural retriever to support generation, and rules on generation quality have been defined as possible remedies @xcite . these have been effective for the general-purpose domain; however, the research surrounding dlms is yet to unfold in mental health. eliza @xcite ) could transform users' statements into questions but employs labor-intensive templates to generate safe and relevant questions. models like rag and realm were developed to include external knowledge to support question generation @xcite . however, these models are still susceptible to incoherent and irrelevant fq generation . further, their end-to-end learning approach is rigid to support process-guided question generation and discourse, often followed in a clinical setting for triage @xcite . in theory, dlms should be capable enough of extracting pieces of information from user description that portrays the understanding of the user and leverage it for generating the next fq. for such a task, supervised training of dlms with process knowledge and coupling it with information retrieval over domain-specific mental health knowledge is a viable solution. this is because mental health knowledge sources (e.g., scid (structured clinical interviews for dsm-5) have structured/semi-structured information on how interviews are performed @xcite . our research substantiates that dlms (e.g., t5) generate low quality follow-up questions in the context of depression for triage, and granting external knowledge through phq-9 reduces the rate at which models generate meaningless fqs @xcite . in the current research, we define an approach for supervised training of dlms on a specific dataset that would yield probability distribution over phq-9 (with support from extended phq-9). these probabilities will confirm whether the dlm can identify cues from user text that can inform a set of phq-9 questions. remaining phq-9 questions are potential fqs. datasets: prior datasets such as counsel chat (counselchat), counseling conversations @xcite , role play @xcite , crisis text line @xcite and reddit c-ssrs @xcite have been created to train ca for mental health counseling. trained cas can engage in a single turn question answering; however, conducting a conversation requires capturing user context and leveraging clinical instruments to guide the generation of fqs. model 1 qgs model 2 qgs model 3 qgs • how long ago did the worsening chronic fatigue continue to cripple my life? [*user already mentions in the title of the post he has been facing the fatigue for years] • what was the estimated dose of suicide? [*unsafe incoherent question] • how could body parts feel far less hot in other parts of my life? [*incoherent question] • what has caused the gradual cognitive decline? [*it's the physician who needs to answer this question after the conversation] • what effect is that i'm less optimistic, and it's embarrassing ? [*incoherent attempt to generate a question from the sentence in the post which mentions "i am still pretty optimistic , but i 'm just too out of it , it 's embarrassing"] • what was the reason that increasing depression has a negative effect on me? [*irrelevant and incoherent] • a sleep study consists of what? [*irrelevant to the diagnostic procedure] • what did caffeine feel in my body? [*wrong pronoun used and irrelevant to the diagnosis] • have you ever tried many depressants or mental health things? [*redundant question as user has already mentioned in the post text that he has tried antidepressants] • are you seeing a therapist? [*redundant question as user had already mentioned in the post text that he has seen a therapist in the past] • do you even ever try to focus on negative things and/or pain, relief, and patience? [*incoherent question] • and what if you forgot to fucking realize that, then it will be fucking so much that you'll be fucking fucking can we ask what they want. [*unsafe objectionable question] • do you mean heavy bipolar disorder? [*irrelevant to the context of the post] • are you aware about the qualities of antidepressants? [*inquisitive in nature but irrelevant from a diagnostic perspective] • what does antidepressant work at? [*irrelevant from a diagnostic perspective] • do you have a close friend you can go to who you can talk with, that way you can get out of the house? [*fq] are you on any antidepressants? [*user mentions in the post he has tried them before but no mention has been made if he is still on them] • do you have any positive thoughts? [*fq] • are you in any danger of hurting yourself? [*slightly unsafe] • is it that you aren't happy with your feelings? [*irrelevant question] • have you tried some exercise? [*redundant question as user already mentions he has tried it] • do you wake up frequently? [*fq] • how is your sleep quality? [*fq] • when you wake up, what do you do? [*fq] • is there anything that helps you calm the symptoms for now? [*fq] • what are your hobbies? [*generic fq] • what are your interests? [*generic fq] extending phq-9 to support fq generation: phq-9 questions are subject to different interpreta- post text model 1 qgs model 2 qgs model 3 qgs i feel depressed . i have felt depressed for a while. i have done a lot of things to try and help myself through therapy ,[...] , staying busy , going to university . all i have found is that each time i complete any of these things that i still feel like i am empty . when i do not do those things for more than 10 mins by myself that my thoughts just get dark and i be little everything i have ever done . [....] also i get depressed because i have dreams where i 'm constantly bombarded by women who are not my wife who want to have relationships with me and it 's a nightly battle to not give in to it when i 'm dreaming . i know it sounds silly but i fear that if i give into the dreams that i might do it in real life . i do n't want to tell my wife because she does n't understand other weird dreams that i have had in the past . • what if i do not do it for more than 10 mins by myself? [*closed book question already answered in the post and irrelevant to the diagnostic procedure] • what might happen to any women that want to have relationships with me? [*irrelevant to the diagnostic process] • what do the women at work do? [*irrelevant to the diagnostic process)] • what kind of dreams are i was depressed? [*closed book question generated directly from a statement in the post] would you be happy with them? [*irrelevant to the diagnostic process)] • what's your job? [*irrelevant] • are you able to reach out to your doctor or psychiatrist? [*user has already mentioned in the original post that he has talked to his therapist] • what are your dreams? [*closed book question already answered in the post] • what do you enjoy about your dreams? [*fq] • do you want to talk to people about what dreams are ? [*fq] • do you feel as satisfied as yourself when you are dreams? [*fq] have you talked to a therapist? [*already answered by the user in the post itself] • is there anything that helps you calm your feeling for now? [*fq] • "have you ever gotten any help?" [*already answered in the query] • do you feel isolated? [*fq] • what are your hobbies? [*fq] • what are your interests? [*fq] • how long have you been waiting for your wife to talk about these dreams? [*fq] • have you told your wife you're depressed or not? [*inquisitive in nature but already answered by the user in original post] ## primate for fq generation we conceptualize our approach on the duality of data and the process knowledge contained in phq-9 (see figure 4 ). first, a bert answerability evaluator identifies which questions in phq-9 are already answerable (using the user's initial description of his/her condition in the post) and which ones need more information to be answerable. the latter type of questions form candidates for training a generative dlm for fq generation. we present primate, a dataset consisting of reddit posts containing user situations describing their health conditions and whether the questions in phq-9 are answerable using the content in the posts. each question is attributed with a binary "yes" or "no" label stating whether the user's description already contains the answer to that question (see bert as answerability evaluator: while model 3 shows respectable performance ( ## conclusion this paper demonstrated the importance of data and process knowledge to adapt dlms for generating fqs that would assist mhps in triaging depression. our experiments show that without process knowledge, dlms hallucinate by generating unsafe, incoherent, and irrelevant questions that are not helpful for mhps in pre-screening or triaging. the challenge lies in the inability of the dlms to judge from the set of generated questions, which is a potential effective fq to ask based on the user information. the improved question generation performance of dlms fine-tuned on conversational data filtered by process knowledge encouraged us to prepare primate. primate can train dlms to judge 'whether a user's description of their mental health condition already contains an answer to a particular question in phq-9', which would eventually guide coherent fq generations. we leave our approach for fq generation as future work, but provide sufficient details on the broader forms of knowledge needed in realizing such a pipeline.