Unsupervised vs. supervised weight estimation for semantic MT evaluation metrics

We present an unsupervised approach to estimate the appropriate degree of contribution of each semantic role type for semantic translation evaluation, yielding a semantic MT evaluation metric whose correlation with human adequacy judgments is comparable to that of recent supervised approaches but without the high cost of a human-ranked training corpus. Our new unsupervised estimation approach is motivated by an analysis showing that the weights learned from supervised training are distributed in a similar fashion to the relative frequencies of the semantic roles. Empirical results show that even without a training corpus of human adequacy rankings against which to optimize correlation, using instead our relative frequency weighting scheme to approximate the importance of each semantic role type leads to a semantic MT evaluation metric that correlates comparable with human adequacy judgments to previous metrics that require far more expensive human rankings of adequacy over a training corpus. As a result, the cost of semantic MT evaluation is greatly reduced.