[Paper]
This paper presents a novel method to generate answers for non-extraction
machine reading comprehension (MRC) tasks whose answers cannot be simply
extracted as one span from the given passages. Using a pointer network-style
extractive decoder for such type of MRC may result in unsatisfactory
performance when the ground-truth answers are given by human annotators or
highly re-paraphrased from parts of the passages. On the other hand, using
generative decoder cannot well guarantee the resulted answers with well-formed
syntax and semantics when encountering long sentences. Therefore, to alleviate
the obvious drawbacks of both sides, we propose an answer making-up method from
extracted multi-spans that are learned by our model as highly confident