Neural machine translation (NMT) models typically operate with a fixed
vocabulary, but translation is an open-vocabulary problem. Previous work
addresses the translation of out-of-vocabulary words by backing off to a
dictionary. In this paper, we introduce a simpler and more effective approach,
making the NMT model capable of open-vocabulary translation by encoding rare
and unknown words as sequences of subword units. This is based on the intuition
that various word classes are translatable via smaller units than words, for
instance names (via character copying or transliteration), compounds (via
compositional translation), and cognates and loanwords (via phonological and
morphological transformations). We discuss the suitability of different word
segmentation techniques, including simple character n-gram models and a
segmentation based on the byte pair encoding compression algorithm, and
empirically show that subword models improve over a back-off dictionary
baseline for the WMT 15 translation tasks English-German and English-Russian by
1.1 and 1.3 BLEU, respectively.

本文提出了一种基于子单词单位的、更简单且更有效的方法来处理翻译中的开放性词汇问题，并通过实验证明了这种方法相较于备用字典模式可以提升该模型的翻译效果。