What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation. (arXiv:2211.04052v1 [cs.CL])

kNN-MT presents a new paradigm for domain adaptation by building an external
datastore, which usually saves all target language token occurrences in the
parallel corpus. As a result, the constructed datastore is usually large and
possibly redundant. In this paper, we investigate the interpretability issue of
this approach: what knowledge does the NMT model need? We propose the notion of
local correctness (LAC) as a new angle, which describes the potential
translation correctness for a single entry and for a …

arxiv domain adaptation knn knowledge memory