Pruning multilingual NMT vocabularies to corpus-relevant tokens plus fine-tuning cuts memory by about 60% and matches or beats a dedicated English-Arabic model on COMET and TER.
An efficient multilingual language model compression through vocabulary trimming
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Efficient Multilingual Neural Machine Translation via Corpus-Driven Vocabulary Pruning: An English-Arabic Case Study
Pruning multilingual NMT vocabularies to corpus-relevant tokens plus fine-tuning cuts memory by about 60% and matches or beats a dedicated English-Arabic model on COMET and TER.