Restricted or Not: A General Training Framework for Neural Machine Translation (2022.acl-srw)
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| Challenge: | Existing work imposes constraints on beam search decoding, which limits the concurrent processing ability of the model in deployment. |
| Approach: | They propose a general training framework that allows a model to support both restricted and unrestricted translations by adopting an additional auxiliary training process without constraining the decoding process. |
| Outcome: | The proposed training framework is tested on simulated and original benchmarks. |
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