Building a Neural Machine Translation System Using Only Synthetic Parallel Data
Jaehong Park Jongyoon Song Sungroh Yoon Department of Electrical and Computer Engineering, Seoul National University {uwanggood, coms1580,
Abstract
Recent works have shown that synthetic parallel data automatically generated by translation models can be effective for various neural machine translation (NMT) issues. In this study, we build NMT systems using only synthetic parallel data. As an efficient alternative to real parallel data, we also present a new type of synthetic parallel corpus. The proposed pseudo parallel data are distinct from previous works in that ground truth and synthetic examples are mixed on both sides of sentence pairs. Experiments on Czech-German and French-German translations demonstrate the efficacy of the proposed pseudo parallel corpus, which shows not only enhanced results for bidirectional translation tasks but also substantial improvement with the aid of a ground truth real parallel corpus.
中文速览
训练神经机器翻译（NMT）系统时，真实双语平行语料往往稀缺，而现有的合成平行数据要么全用源语言真实句子配上机器译文，要么全用目标语言真实句子配上机器译文，这种"一边真、一边假"的结构会导致正向和反向翻译质量严重不均衡，且过度依赖单一翻译模型的质量。为此，研究者提出了一种名为 PSEUDOmix 的新型合成平行语料构建方法：把"源端真实"和"目标端真实"两种合成语料各取一半混合在一起，使句对两侧都同时包含真实句子和合成句子。在捷克语-德语和法语-德语翻译实验中，PSEUDOmix 在双向翻译任务上均优于单一来源的合成语料，且在加入少量真实平行数据进行微调后，其提升幅度也显著超过其他合成数据方案。这项工作表明，在低资源场景下，经过合理混合的合成平行数据可以成为真实平行语料的有效替代或补充，为缺乏双语资源的语言对提供了一条切实可行的出路。
原文 arXiv:1704.00253;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1704.00253v4