Can Latent Alignments Improve Autoregressive Machine Translation? (2021.naacl-main)
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| Challenge: | Latent alignment objectives improve non-autoregressive models, but can they improve autoregressive ones? e.g., we show that latent alignments are incompatible with teacher forcing. |
| Approach: | They propose latent alignment objectives that use a dynamic program to comb the space of monotonic alignments between the "gold" target sequence and token probabilities the model predicts. |
| Outcome: | The proposed models are incompatible with teacher forcing, the authors show . they show that latent alignment objectives reduce misalignments and focus on original error . |
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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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