We present a generic framework for data augmentation via dependency subtree swapping that is applicable to machine translation. We extract corresponding subtrees from the dependency parse trees of the source and target sentences and swap these across bisentences to create augmented samples. We perform thorough filtering based on graphbased similarities of the dependency trees and additional heuristics to ensure that extracted subtrees correspond to the same meaning. We conduct resource-constrained experiments on 4 language pairs in both directions using the IWSLT text translation datasets and the Hunglish2 corpus. The results demonstrate consistent improvements in BLEU score over our baseline models in 3 out of 4 language pairs. Our code is available on GitHub.

翻译：我们提出了一种通用的数据增强框架，通过依存子树交换实现机器翻译中的增强。具体方法是从源句和目标句的依存分析树中提取对应子树，并在双语句对之间交换这些子树以生成增强样本。我们基于依存树的图结构相似性以及额外启发式规则进行严格过滤，确保所提取的子树保持相同的语义。在IWSLT文本翻译数据集和Hunglish2语料库上，我们对4个语言对的双向翻译任务进行了资源受限实验。结果表明，在4个语言对中的3个上，我们的模型相较于基线模型在BLEU分值上取得了一致性提升。相关代码已在GitHub上开源。