Decomposability of Translation Metrics for Improved Evaluation and Efficient Algorithms

B is the de facto standard for evaluation and development of statistical machine translation systems. We describe three real-world situations involving comparisons between different versions of the same systems where one can obtain improvements in B scores that are questionable or even absurd. These situations arise because B lacks the property of decomposability, a property which is also computationally convenient for various applications. We propose a very conservative modification to B and a cross between B and word error rate that address these issues while improving correlation with human judgments.