In a low-resource domain adaptation setup, the simplest dictionary-based word-substitution method (DALI) beats more complex pretraining and copying methods, nearly doubling ChrF, but absolute translation quality remains low.
A Survey on Low-Resource Neural Machine Translation
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abstract
Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data. Thus, a lot of research has been conducted for neural machine translation (NMT) with very limited parallel data, i.e., the low-resource setting. In this paper, we provide a survey for low-resource NMT and classify related works into three categories according to the auxiliary data they used: (1) exploiting monolingual data of source and/or target languages, (2) exploiting data from auxiliary languages, and (3) exploiting multi-modal data. We hope that our survey can help researchers to better understand this field and inspire them to design better algorithms, and help industry practitioners to choose appropriate algorithms for their applications.
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cs.CL 1years
2024 1verdicts
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From Priest to Doctor: Domain Adaptation for Low-Resource Neural Machine Translation
In a low-resource domain adaptation setup, the simplest dictionary-based word-substitution method (DALI) beats more complex pretraining and copying methods, nearly doubling ChrF, but absolute translation quality remains low.