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

arXiv: https://arxiv.org/abs/1904.04969 · HTML: https://mlsanity.com/paper/1904.04969/

## Key facts

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

## Built on by (most cited first, 19 of 19)

- [DAGN: Discourse-Aware Graph Network for Logical Reasoning](https://mlsanity.com/paper/2103.14349/) (2021)
- [Causal Reasoning of Entities and Events in Procedural Texts](https://mlsanity.com/paper/2301.10896/) (2023)
- [A Survey for Efficient Open Domain Question Answering](https://mlsanity.com/paper/2211.07886/) (2022)
- [TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack](https://mlsanity.com/paper/2210.15221/) (2022)
- [Psycholinguistic Tripartite Graph Network for Personality Detection](https://mlsanity.com/paper/2106.04963/) (2021)
- [Coarse-grained decomposition and fine-grained interaction for multi-hop question answering](https://mlsanity.com/paper/2101.05988/) (2021)
- [DF-RAG: Query-Aware Diversity for Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.17212/) (2026)
- [LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension](https://mlsanity.com/paper/2303.06675/) (2023)
- [DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text](https://mlsanity.com/paper/2310.12557/) (2023)
- [ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension](https://mlsanity.com/paper/2107.00841/) (2021)
- [Relational Graph Convolutional Neural Networks for Multihop Reasoning: A Comparative Study](https://mlsanity.com/paper/2210.06418/) (2022)
- [A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches](https://mlsanity.com/paper/2212.04072/) (2022)
- [Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension](https://mlsanity.com/paper/2212.09400/) (2022)
- [Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model](https://mlsanity.com/paper/2307.10443/) (2023)
- [A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents](https://mlsanity.com/paper/2108.09505/) (2021)
- [AdaK-NER: An Adaptive Top-K Approach for Named Entity Recognition with Incomplete Annotations](https://mlsanity.com/paper/2109.05233/) (2021)
- [S2F-NER: Exploring Sequence-to-Forest Generation for Complex Entity Recognition](https://mlsanity.com/paper/2310.18944/) (2023)
- [Perturbation-based Active Learning for Question Answering](https://mlsanity.com/paper/2311.02345/) (2023)
- [Unsupervised multiple choices question answering via universal corpus](https://mlsanity.com/paper/2402.17333/) (2024)

## 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.