Noisy SMS Machine Translation in Low-Density Languages
Vladimir Eidelman, Kristy Hollingshead, Philip Resnik · 2011
This paper presents the system we developed for the 2011 WMT Haitian Creole–English SMS featured translation task. Applying stan-dard statistical machine translation methods to noisy real-world SMS data in a low-density language setting such as Haitian Creole poses a unique set of challenges, which we attempt to address in this work. Along with techniques to better exploit the limited available train-ing data, we explore the benefits of several methods for alleviating the additional noise inherent in the SMS and transforming it to better suite the assumptions of our hierarchi-cal phrase-based model system. We show that these methods lead to significant improve-ments in BLEU score over the baseline. 1