Language Tags Matter for Zero-Shot Neural Machine Translation
This addresses the off-target issue in zero-shot translation for multilingual systems, though it is incremental as it builds on existing language tag methods.
The paper tackles the problem of poor zero-shot translation quality in multilingual neural machine translation by showing that language tag strategies significantly affect performance, achieving a +8 BLEU score improvement with a specific strategy.
Multilingual Neural Machine Translation (MNMT) has aroused widespread interest due to its efficiency. An exciting advantage of MNMT models is that they could also translate between unsupervised (zero-shot) language directions. Language tag (LT) strategies are often adopted to indicate the translation directions in MNMT. In this paper, we demonstrate that the LTs are not only indicators for translation directions but also crucial to zero-shot translation qualities. Unfortunately, previous work tends to ignore the importance of LT strategies. We demonstrate that a proper LT strategy could enhance the consistency of semantic representations and alleviate the off-target issue in zero-shot directions. Experimental results show that by ignoring the source language tag (SLT) and adding the target language tag (TLT) to the encoder, the zero-shot translations could achieve a +8 BLEU score difference over other LT strategies in IWSLT17, Europarl, TED talks translation tasks.