Nearest Neighbor Machine Translation

Nearest Neighbor Machine Translation (kNN-MT) enhances neural machine translation by retrieving similar past translations to improve the quality and speed of new translations, particularly in domain adaptation scenarios. Current research focuses on optimizing kNN-MT's efficiency through techniques like dynamic datastore construction, improved retrieval algorithms (e.g., n-gram based retrieval, hierarchical clustering), and efficient interpolation methods between kNN results and pre-trained models. These advancements aim to reduce computational costs and memory requirements while maintaining or improving translation accuracy, making kNN-MT a more practical approach for real-world applications.