Would you like to tell me more? Generating a corpus of psychotherapy dialogues

The acquisition of a dialogue corpus is a key step in the process of training a dialogue model. In this context, corpora acquisitions have been designed either for open-domain information retrieval or slot-filling (e.g. restaurant booking) tasks. However, there has been scarce research in the problem of collecting personal conversations with users over a long period of time. In this paper we focus on the types of dialogues that are required for mental health applications. One of these types is the follow-up dialogue that a psychotherapist would initiate in reviewing the progress of a Cognitive Behavioral Therapy (CBT) intervention. The elicitation of the dialogues is achieved through textual stimuli presented to dialogue writers. We propose an automatic algorithm that generates textual stimuli from personal narratives collected during psychotherapy interventions. The automatically generated stimuli are presented as a seed to dialogue writers following principled guidelines. We analyze the linguistic quality of the collected corpus and compare the performances of psychotherapists and non-expert dialogue writers. Moreover, we report the human evaluation of a corpus-based response-selection model.

HB Deid - HB De-identification tool demonstrator

This paper describes a freely available web-based demonstrator called HB Deid. HB Deid identifies so-called protected health information, PHI, in a text written in Swedish and removes, masks, or replaces them with surrogates or pseudonyms. PHIs are named entities such as personal names, locations, ages, phone numbers, dates. HB Deid uses a CRF model trained on non-sensitive annotated text in Swedish, as well as a rule-based post-processing step for finding PHI. The final step in obscuring the PHI is then to either mask it, show only the class name or use a rule-based pseudonymisation system to replace it.