Building on the foundations of deep learning and symbolic AI, we have developed software that can answer complex questions with minimal domain-specific training. Our initial results are encouraging – the system achieves state-of-the-art accuracy on two datasets with no need for specialized training.
Building on the foundations of deep learning and symbolic AI, we have developed software that can answer complex questions with minimal domain-specific training. Our initial results are encouraging – the system achieves state-of-the-art accuracy on two datasets with no need for specialized training.
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Date
04 Dec 2020
References
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Fagin, R., Riegel, R. & Gray, A. Foundations of Reasoning with Uncertainty via Real-valued Logics. arXiv:2008.02429 [cs] (2021). ↩
Lee, Y.-S. et al. Pushing the Limits of AMR Parsing with Self-Learning. arXiv:2010.10673 [cs] (2020). ↩
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