Towards Learned Feedback for Enhancing Trust in Information Seeking Dialogue for Radiologists

摘要

Dialogue-based Question Answering (QA) in the context of information seeking applications is a highly complex user interaction task. QA systems normally include various natural language processing components (i.e., components for question classification and information extraction) and information retrieval components. This paper presents a new approach to equip a multimodal QA system for radiologists with some form of self-knowledge about the expected dialogue processing behaviour and the results themselves. The learned models are used to provide feedback of the QA process, i.e., what the system is doing and delivers as results. The resulting automatic feedback behaviour should enhance the user's trust in the system. To this end, examples of the learned feedback are provided in the context of the generation of system-initiative dialogue feedback to a radiologist's questions.