Denoising-based UNMT is more robust to word-order divergence than MASS-based UNMT

We aim to investigate whether UNMT approaches with self-supervised pre-training are robust to word-order divergence between language pairs. We achieve this by comparing two models pre-trained with the same self-supervised pre-training objective. The first model is trained on language pairs with different word-orders, and the second model is trained on the same language pairs with source language re-ordered to match the word-order of the target language. Ideally, UNMT approaches which are robust to word-order divergence should exhibit no visible performance difference between the two configurations. In this paper, we investigate two such self-supervised pre-training based UNMT approaches, namely Masked Sequence-to-Sequence Pre-Training, (MASS) (which does not have shuffling noise) and Denoising AutoEncoder (DAE), (which has shuffling noise). We experiment with five English\(\rightarrow\)Indic language pairs, i.e., en-hi, en-bn, en-gu, en-kn, and en-ta) where word-order of the source language is SVO (Subject-Verb-Object), and the word-order of the target languages is SOV (Subject-Object-Verb). We observed that for these language pairs, DAE-based UNMT approach consistently outperforms MASS in terms of translation accuracies. Moreover, bridging the word-order gap using reordering improves the translation accuracy of MASS-based UNMT models, while it cannot improve the translation accuracy of DAE-based UNMT models. This observation indicates that DAE-based UNMT is more robust to word-order divergence than MASS-based UNMT. Word-shuffling noise in DAE approach could be the possible reason for the approach being robust to word-order divergence.