A Survey on Low-Resource Neural Machine Translation
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摘要: 作为目前主流翻译方法的神经网络机器翻译已经取得了很大突破, 在很多具有丰富数据资源的语言上的翻译质量也不断得到改善, 但对于稀缺资源语言的翻译效果却仍然并不理想. 稀缺资源语言机器翻译是目前机器翻译领域的重要研究热点之一, 近几年来吸引了国内外的广泛关注. 本文对稀缺资源语言机器翻译的研究进行比较全面的回顾, 首先简要介绍了与稀缺资源语言翻译相关的学术活动和数据集, 然后重点梳理了目前主要的研究方法和一些研究结论, 总结了每类方法的特点, 在此基础上总结了不同方法之间的关系并分析了目前的研究现状. 最后, 对稀缺资源语言机器翻译未来可能的研究趋势和发展方向进行了展望,并给出了相关建议.Abstract: As the mainstream approach in the field of Machine Translation, Neural Machine Translation(NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. Low-resource NMT has been one of the most popular issues in MT and attracted wide attention around the world in recent years. This paper presents a survey on low-resource NMT research. We first introduce some related academic activities and feasible data sets for the translation, then categorize and summarize several types of approaches mainly used in low-resource NMT in detail, and present their features, as well as relations between them, and describe the current research status. Finally, we propose some advices on possible research trends and directions of this field in the future.
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图 2 基于枢轴语言的方法(a)和“老师-学生”方法(b)
Fig. 2 Pivot-based method(a) and “Teacher-student” method (b)
图 3 迁移学习, 多语言迁移学习与元学习
Fig. 3 Transfer Learning, Multilingual Transfer Learning and Meta Learning
图 5 数据增强框架. 其中, 1)和2)是传统数据增强方法, 3)和4)是新提出的方法.
Fig. 5 Data Augmentation Method, where 1) and 2) are traditional methods, while 3) and 4) are new ones.
表 1 低资源语言翻译相关的数据资源
Table 1 Data for low-resource MT
数据集 描述 WMT data WMT提供的英语-低资源语言的数据集. 这也是目前研究中使用最多的数据集. IWSLT data 面向口语翻译的IWSLT比赛也提供了一些低资源翻译数据集. WAT data WAT提供亚洲低资源语言的翻译数
据集.LORELEI data⑦ 由DARPA开发的低资源单语-英语双语数据集. JW300[13] 该语料库涵盖了超过300种语言的双语数据. WikiMatrix[14] 该语料库由Facebook开发构建, 包含85种语言的维基百科平行语料. FLORES⑧ 由Facebook开发的英语-尼泊尔语和僧伽罗语的双语数据集. Indian Language Corpora Initiative (ILCI) corpus[15] 该语料库包括11种印度语言与英语的平行语料. Asian Language Treebank[16] 该亚洲语言树库项目包括印尼语、老挝语等9种东南亚语言与英语的平行语料. 
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表 3 几类方法在WMT2019中的使用情况
Table 3 The methods in WMT2019
方法 频次 回译方法 45 多次回译方法 19 迁移学习和微调 24 使用额外语言(包括枢轴语言和多语种) 12 无监督方法 9
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