Leave-one-out Word Alignment without Garbage Collector Effects
Xiaolin Wang, Masao Utiyama, Andrew Finch, Taro Watanabe, Eiichiro Sumita · 2015
Expectation-maximization algorithms, such as those implemented in GIZA++ pervade the field of unsupervised word alignment.However, these algorithms have a problem of over-fitting, leading to "garbage collector effects," where rare words tend to be erroneously aligned to untranslated words.This paper proposes a leave-one-out expectationmaximization algorithm for unsupervised word alignment to address this problem.The proposed method excludes information derived from the alignment of a sentence pair from the alignment models used to align it.This prevents erroneous alignments within a sentence pair from supporting themselves.Experimental results on Chinese-English and Japanese-English corpora show that the F 1 , precision and recall of alignment were consistently increased by 5.0% -17.2%, and BLEU scores of end-to-end translation were raised by 0.03 -1.30.The proposed method also outperformed l 0 -normalized GIZA++ and Kneser-Ney smoothed GIZA++.