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Found 3 papers, 2 papers with code
RenewNAT: Renewing Potential Translation for Non-Autoregressive Transformer
Non-autoregressive neural machine translation (NAT) models are proposed to accelerate the inference process while maintaining relatively high performance.
AMOM: Adaptive Masking over Masking for Conditional Masked Language Model
Experiments on \textbf{3} different tasks (neural machine translation, summarization, and code generation) with \textbf{15} datasets in total confirm that our proposed simple method achieves significant performance improvement over the strong CMLM model.
A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond
While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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