As a special machine translation task, dialect translation has two main characteristics: 1) lack of parallel training corpus; and 2) possessing similar grammar between two sides of the translation. In this paper, we investigate how to exploit the commonality and diversity between dialects thus to build unsupervised translation models merely accessing to monolingual data. Specifically, we leverage pivot-private embedding, layer coordination, as well as parameter sharing to sufficiently model commonality and diversity among source and target, ranging from lexical, through syntactic, to semantic levels. In order to examine the effectiveness of the proposed models, we collect 20 million monolingual corpus for each of Mandarin and Cantonese, which are official language and the most widely used dialect in China. Experimental results reveal that our methods outperform rule-based simplified and traditional Chinese conversion and conventional unsupervised translation models over 12 BLEU scores.
翻译:作为一项特殊的机器翻译任务,方言翻译具有两个主要特点:(1) 缺乏平行的培训材料;(2) 翻译的两侧之间有着类似的语法。在本文中,我们调查如何利用方言之间的共同性和多样性,从而建立仅仅使用单一语言数据的不受监督的翻译模型。 具体地说,我们利用枢纽-私营嵌入、分层协调以及参数共享,充分模拟源和目标之间的共同性和多样性,从词汇学、综合学到语义层面。 为了审查拟议模型的有效性,我们为中国官方语言和最广泛使用的方言各收集了2 000万个普通和广东语单语本。 实验结果表明,我们的方法超越了基于规则的简化和传统的中文转换以及超过12个BLEU分数的常规非监督翻译模型。