Paraphrasing uses different words, sentence structures, or expressions to convey similar semantics. It is an effective training data augmentation method to improve low-resource Natural Language Processing (NLP) tasks. Existing studies normally leverage parallel corpora to construct parabanks, regarding the Machine Translation (MT) results of source sentences as the paraphrases of the corresponding target sentences. As MT models are usually trained on the same parallel corpus, translation of the training set may suffer from overfitting, which leads to less diverse paraphrases. Training paraphrasers on the parabank generated via MT may also suffer from the information loss issue, as the parabank is derived from the parallel corpora, and the knowledge inside the parabank is a subset of that inside the parallel corpora. In this paper, we train bidirectional Multilingual Neural Machine Translation (MNMT) on the bi-directional bilingual parallel corpus, and use the MNMT model directly as a paraphrasing model by asking it to generate "translations" of the input language. As some source tokens also appear in the translation in the parallel corpus, we introduce "copy"/"not-copy" tags to indicate the existence/non-existence of source tokens in the target translation during training, and use the "not-copy" tag to encourage paraphrasing during inference. Manual and automatic evaluation results show that our ParaMNMT method can generate paraphrases of higher semantic consistency, literal fluency and sentential diversity compared to existing parabanks and LLMs. Our data augmentation experiments verify the effectiveness of ParaMNMT on improving low-resource NLP tasks.

“神经机器翻译通常需要大量的平行语料库才能达到良好的翻译效果。而在不同的平行语料库中,均存在词频分布不平衡的问题,这可能导致模型在学习过程中表现出不同的偏差。这些模型倾向于学习高频词汇,而忽略了低频词汇所携带的关键语义信息。忽略的这些低频词汇也包含重要的翻译信息,可能会对翻译质量产生不利影响。目前的方法通常是训练一个双语模型,然后根据频率为词汇分配不同的权重,通过增加低频词的权重来提高低频词的翻译效果。在本文中,我们的目标是提高那些有意义但频率相对较低的词汇的翻译效果。本文提出使用知识蒸馏的方法来提高低频词的翻译效果,训练在低频词上翻译效果更好的模型,将其作为教师模型指导学生模型学习低频词翻译。进而提出一个更加稳定的双教师蒸馏模型,进一步保证高频的性能,使得模型在多个任务上均获得了稳定的提升。本文的单教师蒸馏模型在英语→ 德语任务上相较于SOTA进一步取得了0.64的BLEU提升,双教师蒸馏模型在汉语→ 英语任务上相较于SOTA进一步取得了0.31的BLEU提升,在英语→ 德语、英语→ 捷克语和英语→法语的翻译任务上相较于基线低频词翻译效果,在保证高频词翻译效果不变化的前提下,分别取得了1.24、0.47、0.87的BLEU提升。”