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Sequence-to-Lattice Models for Fast Translation (2021.findings-emnlp)

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Challenge: Existing non-autoregressive machine translation models have decoders that are difficult to port to NAT models.
Approach: They propose a sequence-to-lattice model that replaces the decoder with a search lattice.
Outcome: The proposed model is faster than past non-autoregressive generation approaches and more accurate than reducing the number of decoder layers.

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