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Retrieval-Augmented Machine Translation (RAMT) is attracting growingattention. This is because RAMT not only improves translation metrics, but isalso assumed to implement some form of domain adaptation. In this contribution,we study another salient trait of RAMT, its ability to make translationdecisions more transparent by allowing users to go back to examples thatcontributed to these decisions.  For this, we propose a novel architecture aiming to increase thistransparency. This model adapts a retrieval-augmented version of theLevenshtein Transformer and makes it amenable to simultaneously edit multiplefuzzy matches found in memory. We discuss how to perform training and inferencein this model, based on multi-way alignment algorithms and imitation learning.Our experiments show that editing several examples positively impactstranslation scores, notably increasing the number of target spans that arecopied from existing instances.

Towards Example-Based NMT with Multi-Levenshtein Transformers