Language-independent constraints and regularization in multilingual Transformer NMT yield a 2.23 BLEU average gain on zero-shot pairs from the IWSLT 2017 dataset.
Effective Strategies in Zero-Shot Neural Machine Translation
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abstract
In this paper, we proposed two strategies which can be applied to a multilingual neural machine translation system in order to better tackle zero-shot scenarios despite not having any parallel corpus. The experiments show that they are effective in terms of both performance and computing resources, especially in multilingual translation of unbalanced data in real zero-resourced condition when they alleviate the language bias problem.
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Improving Zero-shot Translation with Language-Independent Constraints
Language-independent constraints and regularization in multilingual Transformer NMT yield a 2.23 BLEU average gain on zero-shot pairs from the IWSLT 2017 dataset.