Diving into the Decoding Space of Non-Autoregressive Models via Lexically Constrained Search (2026.acl-short)
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| Challenge: | Non-autoregressive (NAR) models have been mainly developed to improve decoding efficiency. |
| Approach: | They propose a search-based decoding algorithm which is comparable to the autoregressive Grid Beam Search (GBS) method. |
| Outcome: | The proposed method does not suffer from the MAP degradation issue as the autoregressive method does. |
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| Challenge: | Existing approaches to neural machine translation have computational complexities that are either linear or exponential in the number of constraints. |
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| Challenge: | Existing approaches to lexically constrained neural machine translation suffer from high latency. |
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| Challenge: | Neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model. |
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| Challenge: | Autoregressive generation models generate tokens in a left-to-right, token-by-token fashion, resulting in lag in inference. |
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Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting (N19-1)
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J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
| Challenge: | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in machine translation or monolingual text rewriting tasks. |
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Exploring Non-Autoregressive Text Style Transfer (2021.emnlp-main)
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| Challenge: | Existing methods for text style transfer use autoregressive decoding, but they are slow and low parallelizability. |
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Non-Autoregressive Machine Translation: It’s Not as Fast as it Seems (2022.naacl-main)
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| Challenge: | Efficient machine translation models are commercially important as they can increase inference speeds, reduce costs and carbon emissions. |
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| Challenge: | Non-autoregressive (NAR) neural machine translation models require a conditional independence assumption on target sequences, resulting in less informative learning signals. |
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