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You Only Need One Model for Open-domain Question Answering.

, , , , , and . EMNLP, page 3047-3060. Association for Computational Linguistics, (2022)

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NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension., , , , and . ICLR, OpenReview.net, (2020)You Only Need One Model for Open-domain Question Answering., , , , , and . CoRR, (2021)Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization., , and . EMNLP (Findings), page 407-420. Association for Computational Linguistics, (2021)FiE: Building a Global Probability Space by Leveraging Early Fusion in Encoder for Open-Domain Question Answering., , and . EMNLP, page 4246-4260. Association for Computational Linguistics, (2022)Learning to Generate Questions by Learning to Recover Answer-containing Sentences., , , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 1516-1529. Association for Computational Linguistics, (2021)Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models., , , , , and . CoRR, (2024)You Only Need One Model for Open-domain Question Answering., , , , , and . EMNLP, page 3047-3060. Association for Computational Linguistics, (2022)Keep Learning: Self-supervised Meta-learning for Learning from Inference., and . EACL, page 63-77. Association for Computational Linguistics, (2021)