most citedAPAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning

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cs.CL2023

Human Transcription Quality Improvement

Jian Gao, Hanbo Sun, Cheng Cao +1

High quality transcription data is crucial for training automatic speech recognition (ASR) systems. However, the existing industry-level data collection pipelines are expensive to…

cs.CL2023★ 1 cited

APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning

Sunyi Chi, Bo Dong, Yiming Xu +2

Practical natural language processing (NLP) tasks are commonly long-tailed with noisy labels. Those problems challenge the generalization and robustness of complex models such as D…