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_2: Sentence fluency is an important component of overall text readability but few studies in natural language processing have sought to understand the factors that define it. We report the results of an initial study into the predictive power of surface syntactic statistics for the task; we use fluency assessments done for the purpose of evaluating machine translation. We find that these features are weakly but significantly correlated with fluency. Machine and human translations can be distinguished with accuracy over 80 . The performance of pairwise comparison of fluency is also very high---over 90 for a multi-layer perceptron classifier. We also test the hypothesis that the learned models capture general fluency properties applicable to human-written text. The results do not support this hypothesis: prediction accuracy on the new data is only 57 . This finding suggests that developing a dedicated, task-independent corpus of fluency judgments will be beneficial for further investigations of the problem.
summary: In this approach, researchers aimed to extract detectable features from a parsing tree for use in machine-translated text identification. For example, Chae and Nenova @cite_2 claimed that parsing of machine text is commonly simpler than that of human one. The authors indicated that a simple parsing often contains short main constituents, that is noun, verb, and adjective phrases. Following the intuition, the authors extracted meaningful features, such as parsing tree depth, phrase type proportion, average phrase length, phrase type rate, and phrase length, before using them to distinguish computer- with human-generated text.