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  • title: Non-Monotonic Latent Alignments for CTC-Based NAT
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            Non-Monotonic Latent Alignments for CTC-Based NAT
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            Non-Monotonic Latent Alignments for CTC-Based NAT

            Dec 6, 2022

            Speakers

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            Chenze Shao

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            YF

            Yang Feng

            Speaker · 0 followers

            About

            Non-autoregressive translation (NAT) models are typically trained with the cross-entropy loss, which forces the model outputs to be aligned verbatim with the target sentence and will highly penalize small shifts in word positions. Latent alignment models relax the explicit alignment by marginalizing out all monotonic latent alignments with the CTC loss. However, they cannot handle non-monotonic alignments, which is non-negligible as there is typically global word reordering in machine translatio…

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            NeurIPS 2022

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