knn-seq: Efficient, Extensible kNN-MT Framework

• URL: http://arxiv.org/abs/2310.12352v1
• Date: Wed, 18 Oct 2023 21:56:04 GMT
• Title: knn-seq: Efficient, Extensible kNN-MT Framework
• Authors: Hiroyuki Deguchi, Hayate Hirano, Tomoki Hoshino, Yuto Nishida, Justin Vasselli, Taro Watanabe
• Abstract summary: k-nearest-neighbor machine translation (kNN-MT) boosts the translation quality of a pre-trained machine translation (NMT) model by utilizing translation examples during decoding. Due to its size, it is computationally expensive both to construct and to retrieve examples from the datastore. We present an efficient and achieves kNN-MT framework, knn-seq, for researchers and developers that is carefully designed to run efficiently, even with a billion-scale large datastore.
• Score: 11.421689052786467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: k-nearest-neighbor machine translation (kNN-MT) boosts the translation quality of a pre-trained neural machine translation (NMT) model by utilizing translation examples during decoding. Translation examples are stored in a vector database, called a datastore, which contains one entry for each target token from the parallel data it is made from. Due to its size, it is computationally expensive both to construct and to retrieve examples from the datastore. In this paper, we present an efficient and extensible kNN-MT framework, knn-seq, for researchers and developers that is carefully designed to run efficiently, even with a billion-scale large datastore. knn-seq is developed as a plug-in on fairseq and easy to switch models and kNN indexes. Experimental results show that our implemented kNN-MT achieves a comparable gain to the original kNN-MT, and the billion-scale datastore construction took 2.21 hours in the WMT'19 German-to-English translation task. We publish our knn-seq as an MIT-licensed open-source project and the code is available on https://github.com/naist-nlp/knn-seq . The demo video is available on https://youtu.be/zTDzEOq80m0 .

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