Relational Graph Representation Learning for Open-Domain Question Answering
This addresses the problem of improving question answering accuracy for users in open-domain settings, representing an incremental advancement.
The paper tackles open-domain question answering by introducing a relational graph neural network with bi-directional attention and hierarchical representation learning, achieving state-of-the-art results on the WebQuestionsSP benchmark.
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.