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        <title>Multiloop Incremental Bootstrapping for Low-Resource Machine Translation</title>
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        <namePart type="given">Wuying</namePart>
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            <title>Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track</title>
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            <namePart type="given">Masao</namePart>
            <namePart type="family">Utiyama</namePart>
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    <abstract>Due to the scarcity of high-quality bilingual sentence pairs, some deep-learning-based machine translation algorithms cannot achieve better performance in low-resource machine translation. On this basis, we are committed to integrating the ideas of machine learning algorithm improvement and data augmentation, propose a novel multiloop incremental bootstrapping framework, and design the corresponding semi-supervised learning algorithm. This framework is a meta-frame independent of specific machine translation algorithms. This algorithm makes full use of bilingual seed data of appropriate scale and super-large-scale monolingual data to expand bilingual sentence pair data incrementally, and trains machine translation models step by step to improve the translation quality. The experimental results of neural machine translation on multiple language pairs prove that our proposed framework can make use of continuous monolingual data to raise itself. Its effectiveness is not only reflected in the easy implementation of state-of-the-art low-resource machine translation, but also in the practical option to quickly establish precise domain machine translation systems.</abstract>
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%0 Conference Proceedings
%T Multiloop Incremental Bootstrapping for Low-Resource Machine Translation
%A Liu, Wuying
%A Li, Wei
%A Wang, Lin
%Y Utiyama, Masao
%Y Wang, Rui
%S Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F liu-etal-2023-multiloop
%X Due to the scarcity of high-quality bilingual sentence pairs, some deep-learning-based machine translation algorithms cannot achieve better performance in low-resource machine translation. On this basis, we are committed to integrating the ideas of machine learning algorithm improvement and data augmentation, propose a novel multiloop incremental bootstrapping framework, and design the corresponding semi-supervised learning algorithm. This framework is a meta-frame independent of specific machine translation algorithms. This algorithm makes full use of bilingual seed data of appropriate scale and super-large-scale monolingual data to expand bilingual sentence pair data incrementally, and trains machine translation models step by step to improve the translation quality. The experimental results of neural machine translation on multiple language pairs prove that our proposed framework can make use of continuous monolingual data to raise itself. Its effectiveness is not only reflected in the easy implementation of state-of-the-art low-resource machine translation, but also in the practical option to quickly establish precise domain machine translation systems.
%U https://aclanthology.org/2023.mtsummit-research.1
%P 1-11

Markdown (Informal)

[Multiloop Incremental Bootstrapping for Low-Resource Machine Translation](https://aclanthology.org/2023.mtsummit-research.1) (Liu et al., MTSummit 2023)

• Multiloop Incremental Bootstrapping for Low-Resource Machine Translation (Liu et al., MTSummit 2023)

ACL

• Wuying Liu, Wei Li, and Lin Wang. 2023. Multiloop Incremental Bootstrapping for Low-Resource Machine Translation. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 1–11, Macau SAR, China. Asia-Pacific Association for Machine Translation.