Using Sublexical Translations to Handle the OOV Problem in Machine Translation

Abstract

We introduce a method for learning to translate out-of-vocabulary (OOV) words. The method focuses on combining sublexical/constituent translations of an OOV to generate its translation candidates. In our approach, wildcard searches are formulated based on our OOV analysis, aimed at maximizing the probability of retrieving OOVs’ sublexical translations from existing resources of Machine Translation (MT) systems. At run-time, translation candidates of the unknown words are generated from their suitable sublexical translations and ranked based on monolingual and bilingual information. We have incorporated the OOV model into a state-of-the-art machine translation system and experimental results show that our model indeed helps to ease the impact of OOVs on translation quality, especially for sentences containing more OOVs (significant improvement).