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Recent advances of low-resource neural machine translation

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Machine Translation

Abstract

In recent years, neural network-based machine translation (MT) approaches have steadily superseded the statistical MT (SMT) methods, and represents the current state-of-the-art in MT research. Neural MT (NMT) is a data-driven end-to-end learning protocol whose training routine usually requires a large amount of parallel data in order to build a reasonable-quality MT system. This is particularly problematic for those language pairs that do not have enough parallel text for training. In order to counter the data sparsity problem of the NMT training, MT researchers have proposed various strategies, e.g. augmenting training data, exploiting training data from other languages, alternative learning strategies that use only monolingual data. This paper presents a survey on recent advances of NMT research from the perspective of low-resource scenarios.

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Notes

  1. The 6th Workshop on Asian Translation: http://lotus.kuee.kyoto-u.ac.jp/WAT/WAT2019/index.html.

  2. https://nlp.stanford.edu/projects/nmt/.

  3. http://isw3.naist.jp/~philip-a/emnlp2016/.

  4. www.ted.com.

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Acknowledgements

The ADAPT Centre for Digital Content Technology is funded under the Science Foundation Ireland (SFI) Research Centres Programme (Grant No. 13/RC/2106) and is co-funded under the European Regional Development Fund. This project has partially received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713567, and the publication has emanated from research supported in part by a research grant from SFI under Grant Number 13/RC/2077.

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Authors and Affiliations

  1. School of Computing, National College of Ireland, Mayor Square, IFSC, Dublin 1, Ireland

    Rejwanul Haque

  2. ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland

    Chao-Hong Liu & Andy Way

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  1. Rejwanul Haque
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  2. Chao-Hong Liu
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  3. Andy Way
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Correspondence to Rejwanul Haque.

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Haque, R., Liu, CH. & Way, A. Recent advances of low-resource neural machine translation. Machine Translation 35, 451–474 (2021). https://doi.org/10.1007/s10590-021-09281-1

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