Sequence Level Knowledge Distillation

method ID: sequencelevelknowledgedistillation

Related Papers

Sequence-Level Knowledge Distillation

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

Neural machine translation (NMT) offers a novel alternative formulation of translation that is potentially simpler than statistical ap- proaches. However to reach competitive per- formance, NMT models need to be …...

33587

NAIST English-to-Japanese Simultaneous Translation System for IWSLT 2021 Simultaneous Text-to-text Task

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractThis paper describes NAIST’s system for the English-to-Japanese Simultaneous Text-to-text Translation Task in IWSLT 2021 Evaluation Campaign. Our primary submission is based on wait-k neural machine translation with sequence-level knowledge …...

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POS-Constrained Parallel Decoding for Non-autoregressive Generation

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Publication: TIPSTER (2021)

DOI: nan

AbstractThe multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive …...

8180

One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation

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AbstractNon-autoregressive neural machine translation (NAT) suffers from the multi-modality problem: the source sentence may have multiple correct translations, but the loss function is calculated only according to the reference sentence. …...

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The RoyalFlush System for the WMT 2022 Efficiency Task

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Publication: TIPSTER (2022)

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AbstractThis paper describes the submission of the RoyalFlush neural machine translation system for the WMT 2022 translation efficiency task. Unlike the commonly used autoregressive translation system, we adopted a two-stage …...

27488