Sub-word segmentation is an essential pre-processing step for Neural Machine Translation (NMT). Existing work has shown that neural sub-word segmenters are better than Byte-Pair Encoding (BPE), however, they are inefficient as they require parallel corpora, days to train and hours to decode. This paper introduces SelfSeg, a self-supervised neural sub-word segmentation method that is much faster to train/decode and requires only monolingual dictionaries instead of parallel corpora. SelfSeg takes as input a word in the form of a partially masked character sequence, optimizes the word generation probability and generates the segmentation with the maximum posterior probability, which is calculated using a dynamic programming algorithm. The training time of SelfSeg depends on word frequencies, and we explore several word frequency normalization strategies to accelerate the training phase. Additionally, we propose a regularization mechanism that allows the segmenter to generate various segmentations for one word. To show the effectiveness of our approach, we conduct MT experiments in low-, middle- and high-resource scenarios, where we compare the performance of using different segmentation methods. The experimental results demonstrate that on the low-resource ALT dataset, our method achieves more than 1.2 BLEU score improvement compared with BPE and SentencePiece, and a 1.1 score improvement over Dynamic Programming Encoding (DPE) and Vocabulary Learning via Optimal Transport (VOLT) on average. The regularization method achieves approximately a 4.3 BLEU score improvement over BPE and a 1.2 BLEU score improvement over BPE-dropout, the regularized version of BPE. We also observed significant improvements on IWSLT15 Vi->En, WMT16 Ro->En and WMT15 Fi->En datasets, and competitive results on the WMT14 De->En and WMT14 Fr->En datasets.

翻译：子词分割是神经机器翻译（NMT）中一项关键的预处理步骤。现有研究表明，神经子词分割器优于字节对编码（BPE），但其效率较低，因为它们需要平行语料、数天的训练时间和数小时的解码时间。本文提出SelfSeg，一种自监督神经子词分割方法，其训练/解码速度显著加快，且仅需单语词典而非平行语料。SelfSeg以部分掩码字符序列形式的单词作为输入，优化单词生成概率，并利用动态规划算法计算最大后验概率以生成分割结果。SelfSeg的训练时间取决于词频，我们探索了多种词频归一化策略以加速训练阶段。此外，我们提出一种正则化机制，使分割器能为同一单词生成多种分割结果。为验证方法有效性，我们在低、中、高资源场景下进行机器翻译实验，比较了不同分割方法的性能。实验结果表明，在低资源ALT数据集上，我们的方法相比BPE和SentencePiece平均获得超过1.2 BLEU值的提升，相比动态规划编码（DPE）和基于最优传输的词汇学习（VOLT）平均提升1.1 BLEU值。正则化方法相比BPE获得约4.3 BLEU值的提升，相比BPE的正则化版本BPE-dropout提升1.2 BLEU值。我们在IWSLT15 Vi->En、WMT16 Ro->En和WMT15 Fi->En数据集上观察到显著改进，并在WMT14 De->En和WMT14 Fr->En数据集上取得了具有竞争力的结果。