Papers with NAR text generation

1 papers
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation (2022.emnlp-main)

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Challenge: Existing methods for text generation use auto-regressive (AR) methods, but inefficient inference is a problem.
Approach: They propose an efficient and effective PLM to explicitly model the token dependency during NAR text generation.
Outcome: The proposed model outperforms existing models on three text generation tasks while achieving 10 times faster inference speedup.

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