Class-based named entity translation in a speech to speech translation system

Named entity (NE) translation is a challenging problem in machine translation (MT). Most of the training bi-text corpora for MT lack enough samples of NEs to cover the wide variety of contexts NEs can appear in. In this paper, we present a technique to translate NEs based on their NE types in addition to a phrase-based translation model. Our NE translation model is based on a syntax-based system similar to the work of Chiang (2005); but we produce syntax-based rules with non-terminals as NE types instead of general non-terminals. Such class-based rules allow us to better generalize the context NEs. We show that our proposed method obtains an improvement of 0.66 BLEU score absolute as well as 0.26% in F1-measure over the baseline of phrase-based model in NE test set.