  Generative machine reading comprehension (MRC) requires a model to generate
well-formed answers. For this type of MRC, answer generation method is crucial
to the model performance. However, generative models, which are supposed to be
the right model for the task, in generally perform poorly. At the same time,
single-span extraction models have been proven effective for extractive MRC,
where the answer is constrained to a single span in the passage. Nevertheless,
they generally suffer from generating incomplete answers or introducing
redundant words when applied to the generative MRC. Thus, we extend the
single-span extraction method to multi-span, proposing a new framework which
enables generative MRC to be smoothly solved as multi-span extraction. Thorough
experiments demonstrate that this novel approach can alleviate the dilemma
between generative models and single-span models and produce answers with
better-formed syntax and semantics.
