Detecting Machine-Translated Paragraphs by Matching Similar Words

04/24/2019
by   Hoang-Quoc Nguyen-Son, et al.
0

Machine-translated text plays an important role in modern life by smoothing communication from various communities using different languages. However, unnatural translation may lead to misunderstanding, a detector is thus needed to avoid the unfortunate mistakes. While a previous method measured the naturalness of continuous words using a N-gram language model, another method matched noncontinuous words across sentences but this method ignores such words in an individual sentence. We have developed a method matching similar words throughout the paragraph and estimating the paragraph-level coherence, that can identify machine-translated text. Experiment evaluates on 2000 English human-generated and 2000 English machine-translated paragraphs from German showing that the coherence-based method achieves high performance (accuracy = 87.0 methods (best accuracy = 72.4 on Dutch and Japanese obtain 89.2 results demonstrate the persistence of the proposed method in various languages with different resource levels.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset