Evaluation of Domain Adaptation Approaches to Improve the Translation Quality


Yildirim E. , Tantuğ A. C.

6th International Conference on Computational Collective Intelligence (ICCCI), Seoul, South Korea, 23 - 26 September 2014, vol.572, pp.15-26 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 572
  • Doi Number: 10.1007/978-3-319-10774-5_2
  • City: Seoul
  • Country: South Korea
  • Page Numbers: pp.15-26

Abstract

This paper focuses on the usage of different domain adaptation methods to build a general purpose translation system for the languages with limited parallel training data. Several domain adaptation approaches are evaluated on four different domains in the English-Turkish SMT task. Our comparative experiments show that the language model adaptation gives the best performance and increases the translation success with a relative 9.25% improvement yielding 29.89 BLEU points on multi-domain test data.