Machine Translation Detection from Monolingual Web-Text
Yuki Arase, Ming Zhou · 2013
We propose a method for automatically detecting low-quality Web-text translated by statistical machine translation (SMT) systems. We focus on the phrase salad phenomenon that is observed in existing SMT results and propose a set of computationally inexpensive features to effectively detect such machine-translated sentences from a large-scale Web-mined text. Unlike previous approaches that require bilingual data, our method uses only monolingual text as input; therefore it is applicable for refining data produced by a variety of Web-mining activities. Evaluation results show that the proposed method achieves an accuracy of 95.8 % for sentences and 80.6 % for text in noisy Web pages. 1