Modifying batch weighting and fine tuning with back-translation-based pseudo in-domain selection improves domain-specific UNMT BLEU scores across four scenarios for English-German and English-French.
Domain specialization: a post-training domain adaptation for Neural Machine Translation
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abstract
Domain adaptation is a key feature in Machine Translation. It generally encompasses terminology, domain and style adaptation, especially for human post-editing workflows in Computer Assisted Translation (CAT). With Neural Machine Translation (NMT), we introduce a new notion of domain adaptation that we call "specialization" and which is showing promising results both in the learning speed and in adaptation accuracy. In this paper, we propose to explore this approach under several perspectives.
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Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation
Modifying batch weighting and fine tuning with back-translation-based pseudo in-domain selection improves domain-specific UNMT BLEU scores across four scenarios for English-German and English-French.