A Purely Monotonic Approach to Machine Translation for Similar Languages

Ye Kyaw Thu, A. Finch, E. Sumita, Y. Sagisaka
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Abstract

This paper investigates the effect of taking a strictly monotonic approach to machine translation for a restricted set of suitable language pairs. We studied the effect of decoding monotonically for a set of language pairs which has similar word order characteristics and found that for some language pairs - namely language pairs where both languages are in SOV order - there was almost no difference in machine translation quality. The results of this experiment motivated the extension of the monotonic approach into the alignment stage of the training. We used a Bayesian non-parametric aligner that has been shown to out-perform GIZA++ in combination with the grow-diag-final- and heuristic on transliteration data. Our results show that the monotonic aligner was able to match the performance of the GIZA++ baseline, and gains in translation performance were obtained by integrating both aligners into the systems.
相似语言机器翻译的纯单调方法
本文研究了采用严格单调方法对一组有限的合适语言对进行机器翻译的效果。我们研究了一组具有相似词序特征的语言对的单调解码效果,发现对于某些语言对(即两种语言都是SOV顺序的语言对),机器翻译质量几乎没有差异。本实验的结果促使单调方法扩展到训练的对齐阶段。我们使用了贝叶斯非参数对齐器,该对齐器与grow-diag-final-和对音译数据的启发式相结合,已被证明优于GIZA++。我们的研究结果表明,单调对准器能够匹配giz++基线的性能,并且通过将两种对准器集成到系统中可以获得平移性能的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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