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Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Open accessAbstract
Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-to-end deep neural networks.Specialized neural models have been developed for extracting answers from either text alone or Knowledge Bases (KBs) alone.In this paper we look at a more practical setting, namely QA over the combination of a KB and entitylinked text, which is appropriate when an incomplete KB is available with a large text corpus.Building on recent advances in graph representation learning we propose a novel model, GRAFT-Net, for extracting answers from a question-specific subgraph containing text and KB entities and relations.We construct a suite of benchmark tasks for this problem, varying the difficulty of questions, the amount of training data, and KB completeness.We show that GRAFT-Net is competitive with the state-of-the-art when tested using either KBs or text alone, and vastly outperforms existing methods in the combined setting.
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Cites
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Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation
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Metadata
- Authors
- Sun, H., Dhingra, B., Zaheer, M., Mazaitis, K., Salakhutdinov, R., & Cohen, W.
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 4231-4242
- OpenAlex
- W2890961898
- Cited by
- 466
- References
- 7
- FWCI
- 18.00
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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