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Explainable question answering systems predict an answer together with anexplanation showing why the answer has been selected. The goal is to enableusers to assess the correctness of the system and understand its reasoningprocess. However, we show that current models and evaluation settings haveshortcomings regarding the coupling of answer and explanation which might causeserious issues in user experience. As a remedy, we propose a hierarchical modeland a new regularization term to strengthen the answer-explanation coupling aswell as two evaluation scores to quantify the coupling. We conduct experimentson the HOTPOTQA benchmark data set and perform a user study. The user studyshows that our models increase the ability of the users to judge thecorrectness of the system and that scores like F1 are not enough to estimatethe usefulness of a model in a practical setting with human users. Our scoresare better aligned with user experience, making them promising candidates formodel selection.

F1 is Not Enough! Models and Evaluation Towards User-Centered Explainable Question Answering