Paper: Sentence Level Dialect Identification for Machine Translation System Selection

ACL ID P14-2125
Title Sentence Level Dialect Identification for Machine Translation System Selection
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper we study the use of sentence- level dialect identification in optimizing machine translation system selection when translating mixed dialect input. We test our approach on Arabic, a prototypical diglossic language; and we optimize the combination of four different machine translation systems. Our best result im- proves over the best single MT system baseline by 1.0% BLEU and over a strong system selection baseline by 0.6% BLEU on a blind test set.