面向统计机器翻译的重对齐方法研究

Word Realignment for Statistical Machine Translation

词对齐是统计机器翻译中的重要技术之一。该文提出了一种重对齐方法,它在IBM models获得的正反双向词对齐的基础上,确定出正反双向对齐不一致的部分。之后,对双向词对齐不一致的部分进行重新对齐以得到更好的对称化的词对齐结果。此外,该文提出的方法还可以利用大规模单语语料来强化对齐结果。实验结果表明,相比在统计机器翻译中广泛使用的基于启发信息的词对齐对称化方法,该文提出的方法可以使统计机器翻译系统得到更高的翻译准确率。

Word alignment is one of the key techniques in statistical machine translation （SMT）. In this paper, we propose a word realignment method, which recognizes the inconsistent parts between the bidirectional alignments generated by IBM models at first, and refines then the word alignment by realigning the inconsistent parts. To reinforce our method, a monolingual feature is used to make benefits from large-scale monolingual corpus. The effective- ness of the method is demonstrated on a state-of-the-art phrase-based SMT system. The experimental results show that our method can achieve higher translation accuracy than the widely-adopted heuristics-based method.