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Findings of the 2023 ML-Superb Challenge: Pre-Training And Evaluation Over More Languages And Beyond.

, , , , , , , , , , , , and . ASRU, page 1-8. IEEE, (2023)

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Towards Semi-Supervised Semantics Understanding from Speech., , , and . CoRR, (2020)Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretraining., , , , and . CoRR, (2020)SD-HuBERT: Self-Distillation Induces Syllabic Organization in HuBERT., , , , and . CoRR, (2023)Self-Supervised Speech Representation Learning: A Review., , , , , , , , , and 2 other author(s). CoRR, (2022)QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition., , , , , and . CoRR, (2022)Cooperative Self-training of Machine Reading Comprehension., , , , and . NAACL-HLT, page 244-257. Association for Computational Linguistics, (2022)Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora., , , , , , , and . NAACL-HLT, page 4764-4780. Association for Computational Linguistics, (2022)Thread Structure Prediction for MOOC Discussion Forum., , and . ICYCSEE (2), volume 624 of Communications in Computer and Information Science, page 92-101. Springer, (2016)AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models., , , , , , , , , and 9 other author(s). CoRR, (2023)Multi-stream spectro-temporal and cepstral features based on data-driven hierarchical phoneme clusters., , and . ICASSP, page 5196-5199. IEEE, (2011)