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        <title>Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Jingbo</namePart>
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    <abstract>The standard neural machine translation model can only decode with the same depth configuration as training. Restricted by this feature, we have to deploy models of various sizes to maintain the same translation latency, because the hardware conditions on different terminal devices (e.g., mobile phones) may vary greatly. Such individual training leads to increased model maintenance costs and slower model iterations, especially for the industry. In this work, we propose to use multi-task learning to train a flexible depth model that can adapt to different depth configurations during inference. Experimental results show that our approach can simultaneously support decoding in 24 depth configurations and is superior to the individual training and another flexible depth model training method——LayerDrop.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.385</identifier>
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%0 Conference Proceedings
%T Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation
%A Wang, Qiang
%A Xiao, Tong
%A Zhu, Jingbo
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-training
%X The standard neural machine translation model can only decode with the same depth configuration as training. Restricted by this feature, we have to deploy models of various sizes to maintain the same translation latency, because the hardware conditions on different terminal devices (e.g., mobile phones) may vary greatly. Such individual training leads to increased model maintenance costs and slower model iterations, especially for the industry. In this work, we propose to use multi-task learning to train a flexible depth model that can adapt to different depth configurations during inference. Experimental results show that our approach can simultaneously support decoding in 24 depth configurations and is superior to the individual training and another flexible depth model training method——LayerDrop.
%R 10.18653/v1/2020.findings-emnlp.385
%U https://aclanthology.org/2020.findings-emnlp.385
%U https://doi.org/10.18653/v1/2020.findings-emnlp.385
%P 4307-4312

Markdown (Informal)

[Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation](https://aclanthology.org/2020.findings-emnlp.385) (Wang et al., Findings 2020)

• Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation (Wang et al., Findings 2020)

ACL

• Qiang Wang, Tong Xiao, and Jingbo Zhu. 2020. Training Flexible Depth Model by Multi-Task Learning for Neural Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4307–4312, Online. Association for Computational Linguistics.