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Paying Attention to Wildfire: Using U-Net with Attention Blocks on Multimodal Data for Next Day Prediction., , , , and . ICMI, page 470-480. ACM, (2023)MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages., , , , , , , , , and 6 other author(s). ACL (1), page 4277-4302. Association for Computational Linguistics, (2023)AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model., , , , , , , , , and 6 other author(s). CoRR, (2022)Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems., , , , , , , , , and 31 other author(s). KDD, page 2893-2902. ACM, (2022)Controlling the Extraction of Memorized Data from Large Language Models via Prompt-Tuning., , , , , , , and . ACL (2), page 1512-1521. Association for Computational Linguistics, (2023)The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition., , , and . CoRR, (2022)MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages., , , , , , , , , and 6 other author(s). CoRR, (2022)STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++.. AACL/IJCNLP, page 576-581. Association for Computational Linguistics, (2020)Converting the Point of View of Messages Spoken to Virtual Assistants., , , , , and . CoRR, (2020)Mixture of Domain Experts for Language Understanding: an Analysis of Modularity, Task Performance, and Memory Tradeoffs., , , and . SLT, page 346-352. IEEE, (2022)