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
Most implemented papers
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
THUDM/CogQA
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ACL 2019
We propose a new CogQA framework for multi-hop question answering in web-scale documents.
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs.
Exploiting Explicit Paths for Multi-hop Reading Comprehension
To capture additional context, PathNet also composes the passage representations along each path to compute a passage-based representation.
Compositional Questions Do Not Necessitate Multi-hop Reasoning
shmsw25/single-hop-rc
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ACL 2019
Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs.
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension
jiangycTarheel/EPAr
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ACL 2019
Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context.
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
jd-ai-research-silicon-valley/sae
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Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension
stonybrooknlp/suqa
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EMNLP 2021
Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system.
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?
Other results reveal that our probing questions can help to improve the performance of the models (e. g., by +10. 3 F1) on the main QA task and our dataset can be used for data augmentation to improve the robustness of the models.
