What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation
Wenhao Zhu, Shujian Huang, Yunzhe Lv, Xin Zheng, Jiajun Chen · 2023
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 given neighborhood.Empirical study shows that our investigation successfully finds the conditions where the NMT model could easily fail and need related knowledge.Experiments on six diverse target domains and two language-pairs show that pruning according to local correctness brings a light and more explainable memory for kNN-MT domain adaptation 1 .