  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.
