Recent advances regarding question answering and reading comprehension have
resulted in models that surpass human performance when the answer is contained
in a single, continuous passage of text, requiring only single-hop reasoning.
However, in actual scenarios, lots of complex queries require multi-hop
reasoning. The key to the Question Answering task is semantic feature
interaction between documents and questions, which is widely processed by
Bi-directional Attention Flow (Bi-DAF), but Bi-DAF generally captures only the
surface semantics of words in complex questions and fails to capture implied
semantic feature of intermediate answers. As a result, Bi-DAF partially ignores
part of the contexts related to the question and cannot extract the most
important parts of multiple documents. In this paper we propose a new model
architecture for multi-hop question answering, by applying two completion
strategies: (1) Coarse-Grain complex question Decomposition (CGDe) strategy are
introduced to decompose complex question into simple ones under the condition
of without any additional annotations (2) Fine-Grained Interaction (FGIn)
strategy are introduced to better represent each word in the document and
extract more comprehensive and accurate sentences related to the inference
path. The above two strategies are combined and tested on the SQuAD and
HotpotQA datasets, and the experimental results show that our method
outperforms state-of-the-art baselines.

本文提出了一种新的多跳问题回答模型架构，通过应用 CGDe 和 FGIn 两种策略，在 SQuAD 和 HotpotQA 数据集上表现出超越 state-of-the-art 基线的性能。

面向多跳问题回答的粗粒度分解与细粒度交互

Coarse-grained decomposition and fine-grained interaction for multi-hop  question answering

Machine comprehension (MC), answering a query about a given context
paragraph, requires modeling complex interactions between the context and the
query. Recently, attention mechanisms have been successfully extended to MC.
Typically these methods use attention to focus on a small portion of the
context and summarize it with a fixed-size vector, couple attentions
temporally, and/or often form a uni-directional attention. In this paper we
introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage
hierarchical process that represents the context at different levels of
granularity and uses bi-directional attention flow mechanism to obtain a
query-aware context representation without early summarization. Our
experimental evaluations show that our model achieves the state-of-the-art
results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze
test.

本文介绍了双向注意力流 (BIDAF) 网络，它是一个多阶段分层过程，在不进行早期汇总的情况下，用双向注意力流机制表示不同粒度级别的上下文，并获得了查询感知的上下文表示。实验结果表明，该模型在 Stanford Question Answering Dataset (SQuAD) 和 CNN/DailyMail cloze 测试中取得了最先进的结果。