In the medical field, we have seen the emergence of health-bots that interact with patients to gather data and track their state. One of the downstream application is automatic questionnaire filling, where the content of the dialog is used to automatically fill a pre-defined medical questionnaire. Previous work has shown that answering questions from the dialog context can successfully be cast as a Natural Language Inference (NLI) task and therefore benefit from current pre-trained NLI models. However, NLI models have mostly been trained on text rather than dialogs, which may have an influence on their performance. In this paper, we study the influence of content transformation and content selection on the questionnaire filling task. Our results demonstrate that dialog pre-processing can significantly improve the performance of zero-shot questionnaire filling models which take health-bots dialogs as input.

In clinical studies, chatbots mimicking doctor-patient interactions are used for collecting information about the patient’s health state. Later, this information needs to be processed and structured for the doctor. One way to organize it is by automatically filling the questionnaires from the human-bot conversation. It would help the doctor to spot the possible issues. Since there is no such dataset available for this task and its collection is costly and sensitive, we explore the capacities of state-of-the-art zero-shot models for question answering, textual inference, and text classification. We provide a detailed analysis of the results and propose further directions for clinical questionnaire filling.