Blanc

We introduce BLANC, a family of dynamic, trainable evaluation metrics for machine translation. Flexible, parametrized models can be learned from past data and automatically optimized to correlate well with human judgments for different criteria (e.g. adequacy, fluency) using different correlation measures. Towards this end, we discuss ACS (all common skip-ngrams), a practical algorithm with trainable parameters that estimates reference-candidate translation overlap by computing a weighted sum of all common skip-ngrams in polynomial time. We show that the BLEU and ROUGE metric families are special cases of BLANC, and we compare correlations with human judgments across these three metric families. We analyze the algorithmic complexity of ACS and argue that it is more powerful in modeling both local meaning and sentence-level structure, while offering the same practicality as the established algorithms it generalizes.