Towards Example-Based NMT with Multi-Levenshtein Transformers
Maxime Bouthors Josep Crego François Yvon
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 multi-way 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.