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BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering

arXiv 1904.04969 ↗First posted 10 Apr 2019cs.CL cs.AI cs.LGcited by 19

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Abstract (arXiv)

Multi-hop reasoning question answering requires deep comprehension of relationships between various documents and queries. We propose a Bi-directional Attention Entity Graph Convolutional Network (BAG), leveraging relationships between nodes in an entity graph and attention information between a query and the entity graph, to solve this task. Graph convolutional networks are used to obtain a relation-aware representation of nodes for entity graphs built from documents with multi-level features. Bidirectional attention is then applied on graphs and queries to generate a query-aware nodes representation, which will be used for the final prediction. Experimental evaluation shows BAG achieves state-of-the-art accuracy performance on the QAngaroo WIKIHOP dataset.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Apr 2019.

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Citing papers per year: 2021: 6; 2022: 5; 2023: 6; 2024: 1; 2025: 0; 2026: 1.

19 papers.

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  1. 2021DAGN: Discourse-Aware Graph Network for Logical Reasoningcited by 28
  2. 2023Causal Reasoning of Entities and Events in Procedural Textscited by 23
  3. 2022A Survey for Efficient Open Domain Question Answeringcited by 20
  4. 2022TASA: Deceiving Question Answering Models by Twin Answer Sentences Attackcited by 15
  5. 2021Psycholinguistic Tripartite Graph Network for Personality Detectioncited by 12
  6. 2021Coarse-grained decomposition and fine-grained interaction for multi-hop question answeringcited by 5
  7. 2026DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generationcited by 5
  8. 2023LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehensioncited by 3
  9. 2023DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Textcited by 3
  10. 2021ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehensioncited by 1
  11. 2022Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Studycited by 1
  12. 2022A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approachescited by 1
7 more
  1. 2022Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehensioncited by 1
  2. 2023Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Modelcited by 1
  3. 2021A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documentscited by 0
  4. 2021AdaK-NER: An Adaptive Top-K Approach for Named Entity Recognition with Incomplete Annotationscited by 0
  5. 2023S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity Recognitioncited by 0
  6. 2023Perturbation-based Active Learning for Question Answeringcited by 0
  7. 2024Unsupervised multiple choices question answering via universal corpuscited by 0