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Home/e-Resources/Artificial Intelligence & Machine Learning/UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
Conference ProceedingsArtificial Intelligence & Machine Learning

UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering

Barlas Oğuz·Xilun Chen·Vladimir Karpukhin·Stan Peshterliev·Dmytro Okhonko·Michael Schlichtkrull·Sonal GUPTA·Yashar Mehdad·Scott Yih
Findings of the Association for Computational Linguistics: NAACL2022

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Abstract & Overview

Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Scott Yih. Findings of the Association for Computational Linguistics: NAACL 2022. 2022.

Cite this record

Barlas Oğuz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, et al. (2022). UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering. Findings of the Association for Computational Linguistics: NAACL. https://doi.org/10.18653/v1/2022.findings-naacl.115

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Oğuz, B., Chen, X., Karpukhin, V., Peshterliev, S., Okhonko, D., Schlichtkrull, M., GUPTA, S., Mehdad, Y., & Yih, S. (2022). UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering. Findings of the Association for Computational Linguistics: NAACL. https://doi.org/10.18653/v1/2022.findings-naacl.115
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