Two Ways to Use a Noisy Parallel News corpus for improving Statistical Machine Translation
Souhir Gahbiche, Hélène Bonneau-Maynard, François Yvon, Université Paris-sud · HAL (Le Centre pour la Communication Scientifique Directe) · 2011
In this paper, we present two methods to use a noisy parallel news corpus to improve statistical machine translation (SMT) systems. Taking full advantage of the characteristics of our corpus and of existing resources, we use a bootstrapping strategy, whereby an existing SMT engine is used both to detect parallel sentences in comparable data and to provide an adaptation corpus for translation models. MT experiments demonstrate the benefits of various combinations of these strategies. 1