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_1: Two kinds of bibliographic tools are used to retrieve scientific publications and make them available online. For one kind, access is free as they store information made publicly available online. For the other kind, access fees are required as they are compiled on information provided by the major publishers of scientific literature. The former can easily be interfered with, but it is generally assumed that the latter guarantee the integrity of the data they sell. Unfortunately, duplicate and fake publications are appearing in scientific conferences and, as a result, in the bibliographic services. We demonstrate a software method of detecting these duplicate and fake publications. Both the free services (such as Google Scholar and DBLP) and the charged-for services (such as IEEE Xplore) accept and index these publications.
summary: Another approach recognizes machine-generated text by analyzing a histogram of word distribution. For example, Labb 'e and Labb 'e suggested an inter-textual metric for estimating the similarity of word distributions @cite_1 . This metric is perfectly used for classifying artificial and real papers with accuracy up to 100 method pointed out that a word distribution of human text is closer with a Zipfian distribution than that of machine one. They also offered some valuable features to support the word distribution, that is specific phrases (e.g., idiom, clich 'e , ancient, dialect, phrasal verb) and co-reference resolution. The restriction of distribution-based methods is that they are only stable with a large number of words. However, the deficiency is revealed on homologous texts that refers to same sources such as paraphrasing and translation because such text mostly contains a same set of words.