Recent advances in neural machine translation (NMT) have pushed the quality of machine translation systems to the point where they are becoming widely adopted to build competitive systems. However, there is still a large number of languages that are yet to reap the benefits of NMT. In this paper, we provide the first large-scale case study of the practical application of MT in the Turkic language family in order to realize the gains of NMT for Turkic languages under high-resource to extremely low-resource scenarios. In addition to presenting an extensive analysis that identifies the bottlenecks towards building competitive systems to ameliorate data scarcity, our study has several key contributions, including, i) a large parallel corpus covering 22 Turkic languages consisting of common public datasets in combination with new datasets of approximately 2 million parallel sentences, ii) bilingual baselines for 26 language pairs, iii) novel high-quality test sets in three different translation domains and iv) human evaluation scores. All models, scripts, and data will be released to the public.
翻译:神经机器翻译(NMT)的近期进展将机器翻译系统的质量推向了为建立竞争性系统而广泛采用这种系统的程度,然而,仍有大量语言有待获得NMT的好处。在本文件中,我们提供了土耳其语家庭实际应用MT的第一个大规模案例研究,以便在高资源情况下实现土耳其语NMT在极低资源情景下的成果。除了提供广泛分析,查明在建立竞争性系统以缓解数据稀缺方面存在的瓶颈之外,我们的研究还有几项关键贡献,其中包括:一)一个涵盖22种突厥语的大型平行材料,其中包括公共通用数据集,与大约200万个平行句子的新数据集相结合;二)26对语言的双语基线;三)三个不同翻译领域的新型高质量测试组;四)人类评价分数。所有模型、脚本和数据都将向公众公布。