Papers with Multi-hop reading comprehension

2 papers
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension (P19-1)

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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.
Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension (2022.acl-long)

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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 .
Approach: They propose a deep-learning based inductive logic reasoning method that extracts query-related information and conducts logic reasoning among filtered information.
Outcome: The proposed model is evaluated on two reading comprehension datasets . it uses attentive memories with novel differentiable logic operators .

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