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Machine Translation Detection from Monolingual Web-Text

Yuki Arase and Ming Zhou

The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 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.

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