  Conventional automatic evaluation metrics, such as BLEU and ROUGE, developed
for natural language generation (NLG) tasks, are based on measuring the n-gram
overlap between the generated and reference text. These simple metrics may be
insufficient for more complex tasks, such as question generation (QG), which
requires generating questions that are answerable by the reference answers.
Developing a more sophisticated automatic evaluation metric, thus, remains an
urgent problem in QG research. This work proposes PMAN (Prompting-based Metric
on ANswerability), a novel automatic evaluation metric to assess whether the
generated questions are answerable by the reference answers for the QG tasks.
Extensive experiments demonstrate that its evaluation results are reliable and
align with human evaluations. We further apply our metric to evaluate the
performance of QG models, which shows that our metric complements conventional
metrics. Our implementation of a GPT-based QG model achieves state-of-the-art
performance in generating answerable questions.
