text: 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 ; equal error rate = 13.0 ). It is efficiently better than previous methods (best accuracy = 72.4 ; equal error rate = 29.7 ). Similar experiments on Dutch and Japanese obtain 89.2 and 97.9 accuracy, respectively. The results demonstrate the persistence of the proposed method in various languages with different resource levels.
  @cite_4: This paper introduces a machine learning approach to distinguish machine translation texts from human texts in the sentence level automatically. In stead of traditional methods, we extract some linguistic features only from the target language side to train the prediction model and these features are independent of the source language. Our prediction model presents an indicator to measure how much a sentence generated by a machine translation system looks like a real human translation. Furthermore, the indicator can directly and effectively enhance statistical machine translation systems, which can be proved as BLEU score improvements.
summary: @cite_4 inherited several above features including parsing tree depth and phrase type proportion. In addition, they investigated that the structure of human parsing is more balancing than that of machine one. They thus suggested some useful features: the ratio of right- compared to left-branching nodes, the number of left-branching nodes for noun phrases. The main limitation of parsing-based methods is that they just generate a parsing tree for an individual sentence. An integrated tree cannot be built for larger scope of multiple sentences such as paragraph or document.