Multi-Hop Reading Comprehension
8 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Hop Reading Comprehension
MedHop
ConcurrentQA Benchmark
CodeQueries
Latest papers with no code
Complex Reading Comprehension Through Question Decomposition
Multi-hop reading comprehension requires not only the ability to reason over raw text but also the ability to combine multiple evidence.
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?
Many previous works demonstrated that existing multi-hop reading comprehension datasets (e. g., HotpotQA) contain reasoning shortcuts, where the questions can be answered without performing multi-hop reasoning.
Graph-free Multi-hop Reading Comprehension: A Select-to-Guide Strategy
Multi-hop reading comprehension (MHRC) requires not only to predict the correct answer span in the given passage, but also to provide a chain of supporting evidences for reasoning interpretability.
Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network
Meanwhile, we propose Gated-RGCN to accumulate evidence on the path-based reasoning graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning.
Graph Sequential Network for Reasoning over Sequences
We validate the proposed GSN on two NLP tasks: interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER.
R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason
Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets.
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal
To address MH-QA specifically, we propose a Deep Reinforcement Learning based method capable of learning sequential reasoning across large collections of documents so as to pass a query-aware, fixed-size context subset to existing models for answer extraction.
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph.
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question.
Constructing Datasets for Multi-hop Reading Comprehension Across Documents
We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods.
