Conventional retrieval-augmented neural machine translation (RANMT) systems leverage bilingual corpora, e.g., translation memories (TMs). Yet, in many settings, monolingual corpora in the target language are often available. This work explores ways to take advantage of such resources by directly retrieving relevant target language segments, based on a source-side query. For this, we design improved cross-lingual retrieval systems, trained with both sentence level and word-level matching objectives. In our experiments with two RANMT architectures, we assess of such cross-lingual objectives in a controlled setting, reaching performances that match those of standard TM-based models. We also showcase our method on real-world settings, using much larger monolingual corpora, and observe strong improvements over both the baseline setting, and general-purpose cross-lingual retrievers.

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