Papers with autoregressive baseline

2 papers
Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment (2021.naacl-main)

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Challenge: Non-autoregressive encoder-decoder models improve decoding speed, but generation quality suffers . editing at the level of output sequences limits model flexibility.
Approach: They propose *iterative realignment* which iteratively realigns connectionist temporal alignments.
Outcome: The proposed model matches an autoregressive baseline with a 14x speedup on the WSJ dataset; on LibriSpeech, it achieves an LM-free test-other WER of 9.0% (19% relative improvement on comparable work).
Releasing the Capacity of GANs in Non-Autoregressive Image Captioning (2024.lrec-main)

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Challenge: Existing non-autoregressive (NAR) models suffer from their inherent multi-modality problem.
Approach: They propose an Adversarial Non-autoregressive Transformer for Image Captioning that improves model performance by modifying model structure to be compatible with contrastive learning.
Outcome: The proposed model achieves 26.72 times faster than the autoregressive model on the MSCOCO dataset.

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