Statistical machine translation based text normalization with crowdsourcing

In [1], we have proposed systems for text normalization based on statistical machine translation (SMT) methods which are constructed with the support of Internet users and evaluated those with French texts. Internet users normalize text displayed in a web interface in an annotation process, thereby providing a parallel corpus of normalized and non-normalized text. With this corpus, SMT models are generated to translate non-normalized into normalized text. In this paper, we analyze their efficiency for other languages. Additionally, we embedded the English annotation process for training data in Amazon Mechanical Turk and compare the quality of texts thoroughly annotated in our lab to those annotated by the Turkers. Finally, we investigate how to reduce the user effort by iteratively applying an SMT system to the next sentences to be edited, built from the sentences which have been annotated so far.