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Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining.

, , , , and . EACL, page 1367-1383. Association for Computational Linguistics, (2023)

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Meta Learning to Classify Intent and Slot Labels with Noisy Few Shot Examples., , , , and . SLT, page 1004-1011. IEEE, (2021)On the Robustness of Goal Oriented Dialogue Systems to Real-world Noise., , and . CoRR, (2021)Soft Layer Selection with Meta-Learning for Zero-Shot Cross-Lingual Transfer., , , and . CoRR, (2021)Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data., , , , , , and . EMNLP/IJCNLP (1), page 4525-4535. Association for Computational Linguistics, (2019)Label Semantic Aware Pre-training for Few-shot Text Classification., , , , , , and . ACL (1), page 8318-8334. Association for Computational Linguistics, (2022)Meta learning to classify intent and slot labels with noisy few shot examples., , , , and . CoRR, (2020)Robustification of Multilingual Language Models to Real-world Noise with Robust Contrastive Pretraining., , , , and . CoRR, (2022)Augmented Natural Language for Generative Sequence Labeling., , , and . EMNLP (1), page 375-385. Association for Computational Linguistics, (2020)Learning to Classify Intents and Slot Labels Given a Handful of Examples., , and . CoRR, (2020)Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining., , , , and . EACL, page 1367-1383. Association for Computational Linguistics, (2023)