We present a subword regularization method for WordPiece, which uses a
maximum matching algorithm for tokenization. The proposed method,
MaxMatch-Dropout, randomly drops words in a search using the maximum matching
algorithm. It realizes finetuning with subword regularization for popular
pretrained language models such as BERT-base. The experimental results
demonstrate that MaxMatch-Dropout improves the performance of text
classification and machine translation tasks as well as other subword
regularization methods. Moreover, we provide a comparative analysis of subword
regularization methods: subword regularization with SentencePiece (Unigram),
BPE-Dropout, and MaxMatch-Dropout.

本文提出了一种用于 WordPiece 的子词规则化方法，使用最大匹配算法进行分词，并通过 MaxMatch-Dropout 方法实现对 BERT-base 等预训练语言模型的微调，实验证明该方法在文本分类和机器翻译任务以及其他子词规则化方法中的性能表现明显优于其他方法，同时还进行了 SentencePiece (Unigram)、BPE-Dropout 和 MaxMatch-Dropout 规则化方法的比较分析。

MaxMatch-Dropout: WordPiece 的子词规范化

MaxMatch-Dropout: Subword Regularization for WordPiece

Neural Machine Translation (NMT) models are sensitive to small perturbations
in the input. Robustness to such perturbations is typically measured using
translation quality metrics such as BLEU on the noisy input. This paper
proposes additional metrics which measure the relative degradation and changes
in translation when small perturbations are added to the input. We focus on a
class of models employing subword regularization to address robustness and
perform extensive evaluations of these models using the robustness measures
proposed. Results show that our proposed metrics reveal a clear trend of
improved robustness to perturbations when subword regularization methods are
used.

本文提出了衡量 NMT 模型抗干扰性能的一些额外指标，并针对一类使用子单词规范化方法的模型进行了广泛评估，结果表明我们提出的指标揭示了使用子单词规范化方法时抗干扰性能提高的明显趋势。