  We introduce CHIME, a cross-passage hierarchical memory network for question
answering (QA) via text generation. It extends XLNet introducing an auxiliary
memory module consisting of two components: the context memory collecting
cross-passage evidences, and the answer memory working as a buffer continually
refining the generated answers. Empirically, we show the efficacy of the
proposed architecture in the multi-passage generative QA, outperforming the
state-of-the-art baselines with better syntactically well-formed answers and
increased precision in addressing the questions of the AmazonQA review dataset.
An additional qualitative analysis revealed the interpretability introduced by
the memory module.
