An Unsupervised Approach to Extract Life-Events from Personal Narratives in the Mental Health Domain
Authors: Seyed Mahed Mousavi, Roberto Negro, Giuseppe Riccardi (Università di Trento, 2021)
Topic: Chatbots and Dialogue Systems

Abstract
English. Personal Narratives are an important source of knowledge in the mental health domain. Over an extended period of time, the psychologist learns about the patient's life-events and participants from the Personal Narratives shared during each therapy session. The acquired knowledge is then used to support the patient to reach a healthier mental state by appropriate targeted feedback during each conversation. In this work, we propose an unsupervised approach to automatically extract personal life-events and participants from the patient's narratives and represent them as a personal graph. This personal graph is then updated at each interaction with the patient. We have evaluated our proposed approach on a dataset of longitudinal Italian Personal Narratives as well as a dataset of English commonsense stories.

Introduction
There is a growing research and clinical interest in developing conversational agents (CA) for mental health support as Personal Healthcare Agents (PHA) <cit.>. However, the lack of appropriate domain knowledge has resulted in the abundance of rule-based dialogue systems in the mental health domain with shallow interactions and weak user engagement <cit.>. Currently available dialogue knowledge can be adequate for consumer-oriented agents or holding a free-topic social conversation. However, it can not be used to hold a dialogue about personal life-events and emotions. Meanwhile, patients' conversations in the mental health domain have a unique and complex structure since they encompass personal feelings and situations which vary across patients and interventions. In order to carry out a personal conversation regarding the patient's life-events, it is essential to obtain the required knowledge during each interaction with the patient and from her Personal Narratives. Personal Narratives (PN) are recollections of thoughts and emotions about life-events of the patient. These narratives are used by the psychologist to identify the issues that have activated the patient's emotional state and provide support accordingly in order to reach a healthier mental status <cit.>.  Each Personal Narrative (PN) is presented as a graph of patient's personal space of participants and events. Each edge and the adjacent nodes stand for an event and its corresponding participants in the narrative, respectively. Each edge (event), has an index based on its appearances in the PN which makes it possible to reconstruct the order of occurrences among the events. Moreover, events and participants extracted from prior narratives are considered less relevant for an ongoing interaction and have a lower importance score, presented by dashed lines. In this work, we present an unsupervised approach, inspired by <cit.>, to automatically extract the life-events and their participants from the patient's PNs, and construct a Personal Space Graph. Figure <ref> represents the work flow of our model. Through the interaction with the patient, each narrative is parsed and presented in terms of its predicates (the events, the edges of the graph) and their noun dependencies (the participants, the nodes of the graph). Each edge has an index based on its order of appearance in the narrative which makes it possible to reconstruct the order of occurrences among the events (for instance, the event "litigo spesso" is mentioned after "parla male"). Moreover, the events and participants mentioned in a recent narrative are considered to be more relevant for an ongoing interaction. Based on this assumption, older nodes and edges in the graph will become less relevant upon receiving a new narrative (presented by dashed lines in Figure <ref>). The obtained graph can be integrated with PHAs to automatically identify the life-event that is distressing the patient from hisher PNs to provide support and monitor its recurrence. We have evaluated our approach on a dataset of longitudinal Italian PNs collected from patients who were receiving Cognitive Behavioural Therapy to manage their distress[This data collection has been approved by the Ethical Committee of the University of Trento]. Besides, the English adaptation of our model was evaluated in the "Story Cloze Test" setting introduced by <cit.>. The results show that the proposed approach obtains similar results to other unsupervised models on the English dataset, and can be a strong baseline for personal space representation and response selection in Italian.

Conclusion
In this work, we present an approach to automatically extract life-events and participants from patients' Personal Narratives in the mental health domain and represent them as a personal graph. This graph can be a source of knowledge for Personal Healthcare Agents (PHA) in this domain, to automatically identify the life-event that is activating the user's emotional state and causing distress. We evaluated our model on a domain-specific dataset of Personal Narratives in Italian as well as an open-domain dataset of commonsense stories in English. The results indicate that our proposed model performs in-line with other unsupervised alternatives and can be a strong baseline for automatic extraction of life-events from Personal Narratives in Italian.