All links are not the same: evaluating word alignments for statistical machine translation
Paul C. Davis, Zhuli Xie, Kevin Small · 2007
Word alignments, the mappings between source and target language words for two languages, are a critical component of statistical machine translation. A long-standing issue in statistical machine translation is that the quality of word alignments does not correlate as well as would be expected with measures of translation quality. A number of recent papers have shed light on this issue by improving on existing metrics such as Alignment Error Rate and examining the importance of word alignment quality in terms of phrase alignments. In this paper, we attempt to elucidate this situation further by first presenting a new word alignment evaluation metric, Word Alignment Agreement F1 (WAAF1), which improves upon existing alignment quality metrics. We then present experiments which demonstrate that WAAF1 also correlates better with measures of translation quality than do previous metrics.