论文标题
Chime：用于生成评论的交叉通用层次记忆网络回答
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering
论文作者
论文摘要
我们介绍了Chime，这是一个通过文本生成来解决问题（QA）的交叉通用层次内存网络。它扩展了XLNET引入一个由两个组成部分组成的辅助内存模块：上下文存储器收集交叉通用证据，答案存储器可作为缓冲区不断完善生成的答案。从经验上讲，我们在多通过生成质量质量质量质量质量质量质量质量质量质量质量方面表明了拟议的体系结构的功效，以更好的句法构成良好的答案优于最先进的基线，并提高了解决Amazonqa评论数据集问题的精确度。另一个定性分析揭示了内存模块引入的解释性。
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.
