  Neural machine translation benefits from semantically rich representations.
Considerable progress in learning such representations has been achieved by
language modelling and mutual information maximization objectives using
contrastive learning. The language-dependent nature of language modelling
introduces a trade-off between the universality of the learned representations
and the model's performance on the language modelling tasks. Although
contrastive learning improves performance, its success cannot be attributed to
mutual information alone. We propose a novel Context Enhancement step to
improve performance on neural machine translation by maximizing mutual
information using the Barlow Twins loss. Unlike other approaches, we do not
explicitly augment the data but view languages as implicit augmentations,
eradicating the risk of disrupting semantic information. Further, our method
does not learn embeddings from scratch and can be generalised to any set of
pre-trained embeddings. Finally, we evaluate the language-agnosticism of our
embeddings through language classification and use them for neural machine
translation to compare with state-of-the-art approaches.
