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Many machine translation toolkits make use of a data preparation step whereinraw data is transformed into a tensor format that can be used directly by thetrainer. This preparation step is increasingly at odds with modern research anddevelopment practices because this process produces a static, unchangeableversion of the training data, making common training-time needs difficult(e.g., subword sampling), time-consuming (preprocessing with large data cantake days), expensive (e.g., disk space), and cumbersome (managing experimentcombinatorics). We propose an alternative approach that separates thegeneration of data from the consumption of that data. In this approach, thereis no separate pre-processing step; data generation produces an infinite streamof permutations of the raw training data, which the trainer tensorizes andbatches as it is consumed. Additionally, this data stream can be manipulated bya set of user-definable operators that provide on-the-fly modifications, suchas data normalization, augmentation or filtering. We release an open-sourcetoolkit, SOTASTREAM, that implements this approach:https://github.com/marian-nmt/sotastream. We show that it cuts training time,adds flexibility, reduces experiment management complexity, and reduces diskspace, all without affecting the accuracy of the trained models.

SOTASTREAM: A Streaming Approach to Machine Translation Training