Evaluating Domain Adaptation for Machine Translation Across Scenarios

Abstract

We present an evaluation of the benefits of domain adaptation for machine translation, on three separate domains and language pairs, with varying degrees of domain specificity and amounts of available training data. Domain-adapted statistical and neural machine translation systems are compared to each other and to generic online systems, thus providing an evaluation of the main options in terms of machine translation. Alongside automated translation metrics, we present experimental results involving professional translators, in terms of quality assessment, subjective evaluations of the task and post-editing productivity measurements. The results we present quantify the clear advantages of domain adaptation for machine translation, with marked impacts for domains with higher specificity. Additionally, the results of the experiments show domain-adapted neural machine translation systems to be the optimal choice overall.

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