“Document-level neural machine translation (NMT) has garnered considerable attention sincethe emergence of various context-aware NMT models. However, these static NMT models aretrained on fixed parallel datasets, thus lacking awareness of the target document during infer-ence. In order to alleviate this limitation, we propose a dynamic adapter-translator frameworkfor context-aware NMT, which adapts the trained NMT model to the input document prior totranslation. Specifically, the document adapter reconstructs the scrambled portion of the originaldocument from a deliberately corrupted version, thereby reducing the performance disparity be-tween training and inference. To achieve this, we employ an adaptation process in both the train-ing and inference stages. Our experimental results on document-level translation benchmarksdemonstrate significant enhancements in translation performance, underscoring the necessity ofdynamic adaptation for context-aware translation and the efficacy of our methodologies. Introduction”