BabelNet, SMT, unknown words, OOVs, domain adaptation
Out-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies
different strategies of using BabelNet to alleviate the negative impact brought about by OOVs. BabelNet is a multilingual encyclopedic
dictionary and a semantic network, which not only includes lexicographic and encyclopedic terms, but connects concepts and named
entities in a very large network of semantic relations. By taking advantage of the knowledge in BabelNet, three different methods –
using direct training data, domain-adaptation techniques and the BabelNet API – are proposed in this paper to obtain translations for
OOVs to improve system performance. Experimental results on English–Polish and English–Chinese language pairs show that domain
adaptation can better utilize BabelNet knowledge and performs better than other methods. The results also demonstrate that BabelNet is
a really useful tool for improving translation performance of SMT systems.