Previous methods on knowledge base question generation (KBQG) primarily focus
on enhancing the quality of a single generated question. Recognizing the
remarkable paraphrasing ability of humans, we contend that diverse texts should
convey the same semantics through varied expressions. The above insights make
diversifying question generation an intriguing task, where the first challenge
is evaluation metrics for diversity. Current metrics inadequately assess the
above diversity since they calculate the ratio of unique n-grams in the
generated question itself, which leans more towards measuring duplication
rather than true diversity. Accordingly, we devise a new diversity evaluation
metric, which measures the diversity among top-k generated questions for each
instance while ensuring their relevance to the ground truth. Clearly, the
second challenge is how to enhance diversifying question generation. To address
this challenge, we introduce a dual model framework interwoven by two selection
strategies to generate diverse questions leveraging external natural questions.
The main idea of our dual framework is to extract more diverse expressions and
integrate them into the generation model to enhance diversifying question
generation. Extensive experiments on widely used benchmarks for KBQG
demonstrate that our proposed approach generates highly diverse questions and
improves the performance of question answering tasks.

我们提出了一种新的多样性评估度量，用于评估每个实例的前 K 个生成问题的多样性，并确保它们与事实相关。同时，我们引入了一个双模型框架，通过两种选择策略将外部自然问题与生成模型相结合，以生成多样化的问题。实验结果表明，我们的方法生成了高度多样化的问题，并改善了问题回答任务的性能。