Nearest Neighbor Machine Translation

• URL: http://arxiv.org/abs/2010.00710v2
• Date: Thu, 22 Jul 2021 14:44:51 GMT
• Title: Nearest Neighbor Machine Translation
• Authors: Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis
• Abstract summary: We introduce $k$-nearest-neighbor machine translation ($k$NN-MT) It predicts tokens with a nearest neighbor classifier over a large datastore of cached examples. It consistently improves performance across many settings.
• Score: 113.96357168879548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce $k$-nearest-neighbor machine translation ($k$NN-MT), which predicts tokens with a nearest neighbor classifier over a large datastore of cached examples, using representations from a neural translation model for similarity search. This approach requires no additional training and scales to give the decoder direct access to billions of examples at test time, resulting in a highly expressive model that consistently improves performance across many settings. Simply adding nearest neighbor search improves a state-of-the-art German-English translation model by 1.5 BLEU. $k$NN-MT allows a single model to be adapted to diverse domains by using a domain-specific datastore, improving results by an average of 9.2 BLEU over zero-shot transfer, and achieving new state-of-the-art results -- without training on these domains. A massively multilingual model can also be specialized for particular language pairs, with improvements of 3 BLEU for translating from English into German and Chinese. Qualitatively, $k$NN-MT is easily interpretable; it combines source and target context to retrieve highly relevant examples.

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