The effectiveness of Neural Machine Translation (NMT) models largely depends on the vocabulary used at training; small vocabularies can lead to out-of-vocabulary problems -- large ones, to memory issues. Subword (SW) tokenization has been successfully employed to mitigate these issues. The choice of vocabulary and SW tokenization has a significant impact on both training and fine-tuning an NMT model. Fine-tuning is a common practice in optimizing an MT model with respect to new data. However, new data potentially introduces new words (or tokens), which, if not taken into consideration, may lead to suboptimal performance. In addition, the distribution of tokens in the new data can differ from the distribution of the original data. As such, the original SW tokenization model could be less suitable for the new data. Through a systematic empirical evaluation, in this work we compare different strategies for SW tokenization and vocabulary generation with the ultimate goal to uncover an optimal setting for fine-tuning a domain-specific model. Furthermore, we developed several (in-domain) models, the best of which achieves 6 BLEU points improvement over the baseline.

本文对不同的子词分词策略、词汇生成方法及Fine-tuning进行了系统的实证研究，旨在找到一种最佳的Fine-tuning领域特定模型的设置，实验结果表明最好的模型达到了比基线模型6个BLEU分数的提升效果。

领域内翻译最佳微调的词汇和BPE设置的系统分析——以NMT为例