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ALLSH: Active Learning Guided by Local Sensitivity and Hardness.

, , , , , and . NAACL-HLT (Findings), page 1328-1342. Association for Computational Linguistics, (2022)

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Transfer Understanding from Head Queries to Tail Queries., , , and . CIKM, page 1299-1308. ACM, (2014)BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining., , , , , , , , , and 2 other author(s). CoRR, (2020)OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering., , , , and . NAACL-HLT, page 932-942. Association for Computational Linguistics, (2022)MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation., , , , , and . NAACL-HLT, page 1610-1623. Association for Computational Linguistics, (2022)Scalable Learning to Optimize: A Learned Optimizer Can Train Big Models., , , , , and . ECCV (23), volume 13683 of Lecture Notes in Computer Science, page 389-405. Springer, (2022)XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge., , , and . AAAI, page 10840-10848. AAAI Press, (2022)Adversarial Retriever-Ranker for Dense Text Retrieval., , , , , and . ICLR, OpenReview.net, (2022)Reasoning Like Program Executors., , , , , , , , and . EMNLP, page 761-779. Association for Computational Linguistics, (2022)Soft-Labeled Contrastive Pre-Training for Function-Level Code Representation., , , , , , , , and . EMNLP (Findings), page 118-129. Association for Computational Linguistics, (2022)Large-scale L-BFGS using MapReduce., , and . NIPS, page 1332-1340. (2014)