用单语语料增强数据，让低资源越中翻译模型提升8%。
An Efficient Approach for Machine Translation on Low-resource Languages: A Case Study in Vietnamese-Chinese
- 基于mBART和双语数据训练初始模型，再用TF-IDF筛选相关单语语料。
- 合成的增广数据使模型性能提升8%，优于标准Transformer。
- 适合缺乏平行语料的越中等低资源语言翻译任务。
尽管神经网络在机器翻译中取得进展，但在训练数据不足时表现不佳。本文针对越南语-中文这类低资源语言提出一种高效翻译方法。首先利用双语数据训练初始翻译模型；其次采用TF-IDF技术从越南语和中文单语语料库中筛选与平行语料库领域最相关的句子；最后用初始模型将筛选出的单语语料生成伪平行数据，用于模型训练。实验表明，该方法相较标准Transformer模型性能提升8%，增广数据也显著推动了模型表现。
原文摘要 · Abstract (English)
Despite the rise of recent neural networks in machine translation, those networks do not work well if the training data is insufficient. In this paper, we proposed an approach for machine translation in low-resource languages such as Vietnamese-Chinese. Our proposed method leveraged the power of the multilingual pre-trained language model (mBART) and both Vietnamese and Chinese monolingual corpus. Firstly, we built an early bird machine translation model using the bilingual training dataset. Secondly, we used TF-IDF technique to select sentences from the monolingual corpus which are the most related to domains of the parallel dataset. Finally, the first model was used to synthesize the augmented training data from the selected monolingual corpus for the translation model. Our proposed scheme showed that it outperformed 8% compared to the transformer model. The augmented dataset also pushed the model performance.
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