Relational Graph Representation Learning for Open-Domain Question Answering

Abstract

We introduce a relational graph neural network with bi-directional attention mechanism and hierarchical representation learning for open-domain question answering task. Our model can learn contextual representation by jointly learning and updating the query, knowledge graph, and document representations. The experiments suggest that our model achieves state-of-the-art on the WebQuestionsSP benchmark.

NeurIPS 2019 Workshop on Graph Representation Learning

References in corpus (3)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Variational Reasoning for Question Answering with Knowledge Graph
• Augmenting Neural Machine Translation with Knowledge Graphs

Cited by in corpus (1)

• Contrastive Multi-View Representation Learning on Graphs