Achieving Accurate Conclusions in Evaluation of Automatic Machine Translation Metrics
Automatic Machine Translation metrics, such as BLEU, are widely used in empirical evaluation as a substitute for human assessment. Subsequently, the performance of a given metric is measured by its strength of correlation with human judgment. When a newly proposed metric achieves a stronger correlation over that of a baseline, it is important to take into account the uncertainty inherent in correlation point estimates prior to concluding improvements in metric performance. Confidence intervals for correlations with human judgment are rarely reported in metric evaluations, however, and when they have been reported, the most suitable methods have unfortunately not been applied. For example, incorrect assumptions about correlation sampling distributions made in past evaluations risk over estimation of significant differences in metric performance. In this paper, we provide analysis of each of the issues that may lead to inaccuracies before providing detail of a method that overcomes previous challenges. Additionally, we propose a new method of translation sampling that in contrast achieves genuine high conclusivity in evaluation of the relative performance of metrics.
NAACL