Challenge: Existing models for multi-hop reading comprehension only require a single-hop reasoning, meaning that the evidence needed to answer the question is scattered in a set of supporting documents.
Approach: They propose an interpretable 3-module system called Explore-Propose-Assemble reader (EPAr) that explores and connects relevant information from multiple documents in order to answer a question about the context.
Outcome: The proposed model approximates coarse-to-fine-grained comprehension behavior of human readers when facing multiple long documents.

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Challenge: Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents.
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Challenge: a new method for multi-hop reading comprehension uses deep learning to reason across documents . a previous study showed that deep learning methods only implicitly encode query-related information into embeddings .
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Challenge: Existing approaches to multi-hop reading comprehension do not include multiple sentences or passages.
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Challenge: Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions.
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Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text (P19-1)

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Challenge: Experimental results on 3 popular datasets demonstrate the effectiveness of our approach.
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Challenge: Existing models for multi-hop question answering require multiple pieces of evidence scattered in a given context.
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