用掩码语言模型扩充双语语料库，无需单语数据，可生成海量高质量翻译对。
Parallel Corpus Augmentation using Masked Language Models
- 基于多语言掩码模型替换上下文词，生成候选翻译句对。
- 利用句向量筛选高概率互译句对，提升生成质量。
- 不依赖额外单语数据，适合资源稀缺语言对的语料扩充。
本文提出一种新颖的双语文本语料扩充方法，可在不使用额外单语语料的前提下，生成远超初始种子语料规模的高质量平行语料。该方法利用多语言掩码语言模型在上下文中掩蔽并预测替代词汇，并通过句向量比对筛选极可能互为翻译的句子对。我们采用机器翻译质量评估指标对生成结果进行交叉验证。该方法有望显著缓解所有具备合理种子语料的语言对的数据稀缺问题。
原文摘要 · Abstract (English)
In this paper we propose a novel method of augmenting parallel text corpora which promises good quality and is also capable of producing many fold larger corpora than the seed corpus we start with. We do not need any additional monolingual corpora. We use Multi-Lingual Masked Language Model to mask and predict alternative words in context and we use Sentence Embeddings to check and select sentence pairs which are likely to be translations of each other. We cross check our method using metrics for MT Quality Estimation. We believe this method can greatly alleviate the data scarcity problem for all language pairs for which a reasonable seed corpus is available.
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