Relational Graph Representation Learning for Open-Domain Question Answering
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2019.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1.
4 papers.
- 2022Cross-Domain Few-Shot Graph Classificationcited by 19
- 2022Learning Graph Augmentations to Learn Graph Representationscited by 14
- 2021Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximizationcited by 5
- 2023Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidatacited by 2
- 2023Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata23 May 2023
- 2022Learning Graph Augmentations to Learn Graph Representations24 Jan 2022
- 2022Cross-Domain Few-Shot Graph Classification20 Jan 2022
- 2021Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization24 Mar 2021