CAJ | 학술논문

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.
사대제시통계궤기번역중적중요기술지일.해문제출료일충중대제방법,타재IBM models획득적정반쌍향사대제적기출상,학정출정반쌍향대제불일치적부분.지후,대쌍향사대제불일치적부분진행중신대제이득도경호적대칭화적사대제결과.차외,해문제출적방법환가이이용대규모단어어료래강화대제결과.실험결과표명,상비재통계궤기번역중엄범사용적기우계발신식적사대제대칭화방법,해문제출적방법가이사통계궤기번역계통득도경고적번역준학솔.
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 effectiveness 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.