BAG: Bi-directional Attention Entity Graph Convolutional Network For Multi-hop Reasoning Question Answering | Awesome LLM Papers Add your paper to Awesome LLM Papers

Awesome LLM Papers

A continuously updated collection of research papers on LLMs.

Stay Updated

Contact Sean Moran about this survey or website. Made with Jekyll and Hyde.

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

Yu Cao, Meng Fang, Dacheng Tao . Arxiv 2019 – 42 citations

[Paper]   Search on Google Scholar   Search on Semantic Scholar
Compositional Generalization Datasets Evaluation Question Answering

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.

Similar Work