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        <title>A Novel Two-step Fine-tuning Framework for Transfer Learning in Low-Resource Neural Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Yuan</namePart>
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    <abstract>Existing transfer learning methods for neural machine translation typically use a well-trained translation model (i.e., a parent model) of a high-resource language pair to directly initialize a translation model (i.e., a child model) of a low-resource language pair, and the child model is then fine-tuned with corresponding datasets. In this paper, we propose a novel two-step fine-tuning (TSFT) framework for transfer learning in low-resource neural machine translation. In the first step, we adjust the parameters of the parent model to fit the child language by using the child source data. In the second step, we transfer the adjusted parameters to the child model and fine-tune it with a proposed distillation loss for efficient optimization. Our experimental results on five low-resource translations demonstrate that our framework yields significant improvements over various strong transfer learning baselines. Further analysis demonstrated the effectiveness of different components in our framework.</abstract>
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    <identifier type="doi">10.18653/v1/2024.findings-naacl.203</identifier>
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%0 Conference Proceedings
%T A Novel Two-step Fine-tuning Framework for Transfer Learning in Low-Resource Neural Machine Translation
%A Gao, Yuan
%A Hou, Feng
%A Wang, Ruili
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F gao-etal-2024-novel
%X Existing transfer learning methods for neural machine translation typically use a well-trained translation model (i.e., a parent model) of a high-resource language pair to directly initialize a translation model (i.e., a child model) of a low-resource language pair, and the child model is then fine-tuned with corresponding datasets. In this paper, we propose a novel two-step fine-tuning (TSFT) framework for transfer learning in low-resource neural machine translation. In the first step, we adjust the parameters of the parent model to fit the child language by using the child source data. In the second step, we transfer the adjusted parameters to the child model and fine-tune it with a proposed distillation loss for efficient optimization. Our experimental results on five low-resource translations demonstrate that our framework yields significant improvements over various strong transfer learning baselines. Further analysis demonstrated the effectiveness of different components in our framework.
%R 10.18653/v1/2024.findings-naacl.203
%U https://aclanthology.org/2024.findings-naacl.203
%U https://doi.org/10.18653/v1/2024.findings-naacl.203
%P 3214-3224

Markdown (Informal)

[A Novel Two-step Fine-tuning Framework for Transfer Learning in Low-Resource Neural Machine Translation](https://aclanthology.org/2024.findings-naacl.203) (Gao et al., Findings 2024)

• A Novel Two-step Fine-tuning Framework for Transfer Learning in Low-Resource Neural Machine Translation (Gao et al., Findings 2024)

ACL

• Yuan Gao, Feng Hou, and Ruili Wang. 2024. A Novel Two-step Fine-tuning Framework for Transfer Learning in Low-Resource Neural Machine Translation. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3214–3224, Mexico City, Mexico. Association for Computational Linguistics.