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.

介绍一种名为 CHIME 的基于记忆网络的文本生成问答系统，通过引入辅助记忆模块综合跨越段落证据和不断改进所生成答案的缓存区两部分， 在 AmazonQA 评论数据集的多段问答中，其效果优于现有最佳方法并在句法结构正规及精度方面有所提高。

CHIME: 交叉通道分层记忆网络用于生成式评论问答

CHIME: Cross-passage Hierarchical Memory Network for Generative Review  Question Answering

BERT model has been successfully applied to open-domain QA tasks. However,
previous work trains BERT by viewing passages corresponding to the same
question as independent training instances, which may cause incomparable scores
for answers from different passages. To tackle this issue, we propose a
multi-passage BERT model to globally normalize answer scores across all
passages of the same question, and this change enables our QA model find better
answers by utilizing more passages. In addition, we find that splitting
articles into passages with the length of 100 words by sliding window improves
performance by 4%. By leveraging a passage ranker to select high-quality
passages, multi-passage BERT gains additional 2%. Experiments on four standard
benchmarks showed that our multi-passage BERT outperforms all state-of-the-art
models on all benchmarks. In particular, on the OpenSQuAD dataset, our model
gains 21.4% EM and 21.5% $F_1$ over all non-BERT models, and 5.8% EM and 6.5%
$F_1$ over BERT-based models.

本文提出了一种多样化的 BERT 模型，通过使用多个片段并全局规范化答案分数，并得到统一的分数，性能提高了 4％。通过使用段落评分器选择高质量的段落，多通道 BERT 获得了额外的 2％。实验结果表明，我们的方案在所有基准测试上均优于现有模型，特别是在 OpenSQuAD 数据集上，我们的模型比所有非 BERT 模型和基于 BERT 的模型都表现出色。