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Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3169419
Abstract: The non-autoregressive decoders in neural machine translation are paid increasing attention due to their faster decoding than autoregressive decoders. However, their apparent problem is a low performance which is mainly originated from wrong prediction about…
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Keywords:
target sentence;
non autoregressive;
translation model;
length ... See more keywords
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Published in 2021 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2020.3044547
Abstract: Very deep transformers outperform conventional bi-directional long short-term memory networks for automatic speech recognition (ASR) by a significant margin. However, being autoregressive models, their computational complexity is still a prohibitive factor in their deployment into…
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Keywords:
autoregressive transformer;
speech recognition;
non autoregressive;
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Published in 2023 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2023.3272798
Abstract: The autoregressive-based model with the left-to-right generation order has been a predominant paradigm for generative linguistic steganography. However, such steganography does not perform well on semantic control and content planning, which is forced by the…
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Keywords:
steganography;
generative linguistic;
progressive non;
non autoregressive ... See more keywords
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Published in 2022 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.48550/arxiv.2204.09269
Abstract: Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate…
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Keywords:
machine;
generation;
non autoregressive;
survey ... See more keywords