Though nearest neighbor Machine Translation ($k$NN-MT) \cite{khandelwal2020nearest} has proved to introduce significant performance boosts over standard neural MT systems, it is prohibitively slow since it uses the entire reference corpus as the datastore for the nearest neighbor search. This means each step for each beam in the beam search has to search over the entire reference corpus. $k$NN-MT is thus two-order slower than vanilla MT models, making it hard to be applied to real-world applications, especially online services. In this work, we propose Fast $k$NN-MT to address this issue. Fast $k$NN-MT constructs a significantly smaller datastore for the nearest neighbor search: for each word in a source sentence, Fast $k$NN-MT first selects its nearest token-level neighbors, which is limited to tokens that are the same as the query token. Then at each decoding step, in contrast to using the entire corpus as the datastore, the search space is limited to target tokens corresponding to the previously selected reference source tokens. This strategy avoids search through the whole datastore for nearest neighbors and drastically improves decoding efficiency. Without loss of performance, Fast $k$NN-MT is two-order faster than $k$NN-MT, and is only two times slower than the standard NMT model. Fast $k$NN-MT enables the practical use of $k$NN-MT systems in real-world MT applications.\footnote{Code is available at \url{https://github.com/ShannonAI/fast-knn-nmt.}}

本文提出了一种快速的$k$最近邻机器翻译模型，通过使用仅限于相同查询令牌的最近令牌级邻居来限制查询空间，从而大大提高了解码效率，并实现了与$k$NN-MT相当的性能表现，只比标准NMT模型慢两倍。

快速最近邻机器翻译