Multi-hop Question Answering
56 papers with code • 2 benchmarks • 4 datasets
Libraries
Use these libraries to find Multi-hop Question Answering models and implementationsMost implemented papers
Repurposing Entailment for Multi-Hop Question Answering Tasks
We introduce Multee, a general architecture that can effectively use entailment models for multi-hop QA tasks.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
We propose a new CogQA framework for multi-hop question answering in web-scale documents.
Multi-hop Question Answering via Reasoning Chains
Our analysis shows the properties of chains that are crucial for high performance: in particular, modeling extraction sequentially is important, as is dealing with each candidate sentence in a context-aware way.
Commonsense for Generative Multi-Hop Question Answering Tasks
We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer.
HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data
3) a hybrid model that combines heterogeneous information to find the answer.
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction.
MuSiQue: Multihop Questions via Single-hop Question Composition
Multihop reasoning remains an elusive goal as existing multihop benchmarks are known to be largely solvable via shortcuts.
Rethinking Label Smoothing on Multi-hop Question Answering
Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiring multiple reasoning components, including document retrieval, supporting sentence prediction, and answer span extraction.
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering
To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets.