Nearest Neighbor Machine Translation (2010.00710v2)

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.

An Examination of $k$-Nearest-Neighbor Machine Translation

The paper introduces $k$-nearest-neighbor machine translation ($k$NN-MT), a novel integration of non-parametric methods into neural machine translation (MT) systems, leveraging a nearest neighbor classifier over an extensive datastore of cached examples. This approach is effectively applied to enhance existing pre-trained neural translation models, refining their generalization capabilities. It achieves a balance between expressiveness and adaptability, due to its ability to access a vast repository of translation instances during test time without necessitating additional training periods.

The methodology of $k$NN-MT interpolates the standard target-token softmax distribution generated by a neural MT model with a multinomial derived from nearest neighbor search results. This process involves indexing translation contexts using hidden states obtained from the base model. The underlying hypothesis is straightforward: similar contexts, in terms of representation space, likely result in similar subsequent target words. Thus, an enriched dataset can improve model outputs even beyond the bounds of original training, and it facilitates domain-specific adaptation with ease.

In terms of performance, the application of $k$NN-MT on a state-of-the-art German-English model resulted in a notable 1.5 BLEU score improvement. Critically, this boost is achieved by fielding German-English datastore examples without excessive training. Domain transfer adaptation, an area frequently challenged by the necessity for domain-specific training, witnesses substantial advancements with an average increase of 9.2 BLEU across various domains. Moreover, the approach demonstrates the capacity to specialize multilingual models for particular language pairs, leading to improvements exemplified by a 3 BLEU rise for English-German and English-Chinese translations.

From a practical perspective, $k$NN-MT heralds significant implications. Primarily, it obviates the need for additional training when extending a model’s domain or language pairing scope, thus enhancing computational efficiency and resource allocation. The model demonstrates robust adaptability through the manipulation of datastore contents, emphasizing its suitability for dynamic environments where training data availability or requirements can shift rapidly.

Theoretically, this approach underscores the potential of non-parametric augmentation within neural networks. By incorporating local contextual examples into the prediction phase, neural MT models can achieve higher pertinence in their outputs, eventually rendering themselves more versatile across multifaceted datasets. The retrieval mechanisms employed are computationally scalable, aligning with the broader objectives in machine learning to optimize resource-heavy processes.

In speculative regard towards future AI development, $k$NN-MT hints at an evolving landscape where models increasingly benefit from an amalgamation of parametric and non-parametric technologies. Such hybrids have the innate ability to grasp broader contextual relevance, fostering more coherent and contextually grounded translation outputs.

In conclusion, $k$-nearest-neighbor machine translation is positioned as an innovative step forward in the landscape of natural language processing. Its capacity to harness extensive example repositories opens avenues for broader applications in AI, where adaptability and contextual awareness play pivotal roles. Future endeavors may well explore the optimization of retrieval mechanisms or further applications across other generative tasks, cementing the role of non-parametric integration within AI systems.