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Question Generation (QG) aims to automate the task of composing questions fora passage with a set of chosen answers found within the passage. In recentyears, the introduction of neural generation models has resulted in substantialimprovements of automatically generated questions in terms of quality,especially compared to traditional approaches that employ manually craftedheuristics. However, the metrics commonly applied in QG evaluations have beencriticized for their low agreement with human judgement. We therefore propose anew reference-free evaluation metric that has the potential to provide a bettermechanism for evaluating QG systems, called QAScore. Instead of fine-tuning alanguage model to maximize its correlation with human judgements, QAScoreevaluates a question by computing the cross entropy according to theprobability that the language model can correctly generate the masked words inthe answer to that question. Furthermore, we conduct a new crowd-sourcing humanevaluation experiment for the QG evaluation to investigate how QAScore andother metrics can correlate with human judgements. Experiments show thatQAScore obtains a stronger correlation with the results of our proposed humanevaluation method compared to existing traditional word-overlap-based metricssuch as BLEU and ROUGE, as well as the existing pretrained-model-based metricBERTScore.

QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation