EXtrA-ShaRC: Explainable and Scrutable Reading Comprehension for Conversational Systems

Conversational Machine Reading (CMR) systems answer high-level user questions by interpreting contextual information, asking clarification questions, and generating human-like responses. While effective, such systems often use knowledge about the task and the user in a non-transparent and non-scrutable way. For example, if a user wants to ask questions like "Why are you asking this?" or "Why is this the correct answer?", the system should be able to highlight and return the relevant information that led to the decision in an interpretable manner. Similarly, if a user scrutinizes and edits their user profile, the final output of the model should change accordingly. To test the transparency and scrutability of conversational machine reading systems, we formalize two new tasks by extending the ShARC dataset to create the EXtrA-ShARC dataset. For transparency, we propose a baseline model that can simultaneously extract explanations and answer the user's question. We will also publicly release counterfactual user profiles to test scrutability for all CMR models. Our dataset opens up a range of research directions for using natural language explanations and counterfactual profiles in conversational systems, both for evaluating the model and increasing transparency for end users.