基于非连续短语的统计翻译模型研究

目前统计机器翻译的主流方法仍然是基于短语的翻译模型。然而,该模型并没有考虑对非连续短语的处理。本文提出了一种基于非连续短语的统计翻译模型,利用该模型可以使翻译的基本单元从连续短语扩展到带有间隔的非连续短语,以更好地解决词语翻译时的上下文依赖问题。同时,由于该方法抽取的短语数量较少,也使得解码的效率得到了提高。实验表明,在效率提高的情况下,非连续短语模型可以取得与层次型短语模型相当的翻译结果。

Research on Non-contiguous Phrase-based Model for Statistical Machine Translation

The phrase-based statistical machine translation model is still the most popular model nowadays.However,non-contiguous phrases are not taken into account in this model.A statistical machine translation model based on non-contiguous phrases is proposed in this paper.The units of translation are extended from contiguous phrases to phrases with intervals in order to take advantage of the context dependence.With the less numbers of phrases,the efficiency of the decoder in our model is also improved.Experiments show that with a better efficiency the translation results of our non-contiguous phrase-based model and hierarchical model are comparable.