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Although neural machine translation(NMT) yields promising translationperformance, it unfortunately suffers from over- and under-translation is- sues[Tu et al., 2016], of which studies have become research hotspots in NMT. Atpresent, these studies mainly apply the dominant automatic evaluation metrics,such as BLEU, to evaluate the overall translation quality with respect to bothadequacy and uency. However, they are unable to accurately measure the abilityof NMT systems in dealing with the above-mentioned issues. In this paper, wepropose two quantitative metrics, the Otem and Utem, to automatically evaluatethe system perfor- mance in terms of over- and under-translation respectively.Both metrics are based on the proportion of mismatched n-grams between goldref- erence and system translation. We evaluate both metrics by comparing theirscores with human evaluations, where the values of Pearson Cor- relationCoefficient reveal their strong correlation. Moreover, in-depth analyses onvarious translation systems indicate some inconsistency be- tween BLEU and ourproposed metrics, highlighting the necessity and significance of our metrics.

Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT