论文标题
半监督域适应机器的简单基线
A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
论文作者
论文摘要
最先进的神经机器翻译（NMT）系统是渴望数据的，并且在没有监督数据的新域上表现较差。由于数据收集昂贵且在许多情况下是不可行的，因此需要域的适应方法。在这项工作中，我们为NMT的半监督域适应方案提出了一种简单但效果的方法，其目的是在目标域上改善翻译模型的性能，仅在监督源域数据的帮助下仅由非平行数据组成。这种方法通过三个培训目标进行迭代训练基于变压器的NMT模型：语言建模，反向翻译和监督翻译。我们在两个适应设置上评估了此方法：特定领域与从一般领域的适应之间的适应性，以及两种语言对：德语至英语，罗马尼亚语对英语。在最强的基线上实现了实质性的改进 - 最高+19.31 BLEU，并且对非适应模型的BLLEU改进 - 我们将这种方法作为一种简单但很难脱颖而出的基线在NMT的半监测领域适应领域中。
State-of-the-art neural machine translation (NMT) systems are data-hungry and perform poorly on new domains with no supervised data. As data collection is expensive and infeasible in many cases, domain adaptation methods are needed. In this work, we propose a simple but effect approach to the semi-supervised domain adaptation scenario of NMT, where the aim is to improve the performance of a translation model on the target domain consisting of only non-parallel data with the help of supervised source domain data. This approach iteratively trains a Transformer-based NMT model via three training objectives: language modeling, back-translation, and supervised translation. We evaluate this method on two adaptation settings: adaptation between specific domains and adaptation from a general domain to specific domains, and on two language pairs: German to English and Romanian to English. With substantial performance improvement achieved---up to +19.31 BLEU over the strongest baseline, and +47.69 BLEU improvement over the unadapted model---we present this method as a simple but tough-to-beat baseline in the field of semi-supervised domain adaptation for NMT.
