  Neural machine translation models are often biased toward the limited
translation references seen during training. To amend this form of overfitting,
in this paper we propose fine-tuning the models with a novel training objective
based on the recently-proposed BERTScore evaluation metric. BERTScore is a
scoring function based on contextual embeddings that overcomes the typical
limitations of n-gram-based metrics (e.g. synonyms, paraphrases), allowing
translations that are different from the references, yet close in the
contextual embedding space, to be treated as substantially correct. To be able
to use BERTScore as a training objective, we propose three approaches for
generating soft predictions, allowing the network to remain completely
differentiable end-to-end. Experiments carried out over four, diverse language
pairs have achieved improvements of up to 0.58 pp (3.28%) in BLEU score and up
to 0.76 pp (0.98%) in BERTScore (F_BERT) when fine-tuning a strong baseline.
