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Multilingual neural machine translation models are trained to maximize thelikelihood of a mix of examples drawn from multiple language pairs. Thedominant inductive bias applied to these models is a shared vocabulary and ashared set of parameters across languages; the inputs and labels correspondingto examples drawn from different language pairs might still reside in distinctsub-spaces. In this paper, we introduce multilingual crossover encoder-decoder(mXEncDec) to fuse language pairs at an instance level. Our approachinterpolates instances from different language pairs into joint `crossoverexamples' in order to encourage sharing input and output spaces acrosslanguages. To ensure better fusion of examples in multilingual settings, wepropose several techniques to improve example interpolation across dissimilarlanguages under heavy data imbalance. Experiments on a large-scale WMTmultilingual dataset demonstrate that our approach significantly improvesquality on English-to-Many, Many-to-English and zero-shot translation tasks(from +0.5 BLEU up to +5.5 BLEU points). Results on code-switching setsdemonstrate the capability of our approach to improve model generalization toout-of-distribution multilingual examples. We also conduct qualitative andquantitative representation comparisons to analyze the advantages of ourapproach at the representation level.

Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation