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Neural knowledge-grounded generative models for dialogue often producecontent that is factually inconsistent with the knowledge they rely on, makingthem unreliable and limiting their applicability. Inspired by recent work onevaluating factual consistency in abstractive summarization, we propose anautomatic evaluation metric for factual consistency in knowledge-groundeddialogue using automatic question generation and question answering. Ourmetric, denoted $Q^2$, compares answer spans using natural language inference(NLI), instead of token-based matching as done in previous work. To fosterproper evaluation, we curate a novel dataset of dialogue system outputs for theWizard-of-Wikipedia dataset, manually annotated for factual consistency. Weperform a thorough meta-evaluation of $Q^2$ against other metrics using thisdataset and two others, where it consistently shows higher correlation withhuman judgements.

$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering