Transliteration is a task of translating named entities from a language to another, based on phonetic similarity. The task has embraced deep learning approaches in recent years, yet, most ignore the phonetic features of the involved languages. In this work, we incorporate phonetic information into neural networks in two ways: we synthesize extra data using forward and back-translation but in a phonetic manner; and we pre-train models on a phonetic task before learning transliteration. Our experiments include three language pairs and six directions, namely English to and from Chinese, Hebrew and Thai. Results indicate that our proposed approach brings benefits to the model and achieves better or similar performance when compared to state of the art.
翻译:翻写是一项基于音效相似性的将名称实体从一种语言翻译成另一种语言的任务。任务包括近些年来的深层次学习方法,但多数人忽略了所涉语言的语音特征。在这项工作中,我们以两种方式将语音信息纳入神经网络:我们用前向和后向翻译方式合成额外数据,但以语音方式;我们在学习转写之前先用语音任务预演模型。我们的实验包括三种语言配对和六种方向,即中文、希伯莱文和泰文之间的英语。结果显示,我们提出的方法给模型带来了好处,在与艺术现状相比,取得了更好或类似的业绩。