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_0: 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.
  @cite_3: Computer-generated text plays a pivotal role in various applications, but the quality of the generated text is much lower than that of human-generated text. The use of artificially generated “machine text” can thus negatively affect such practical applications as website generation and text corpora collection. A method for distinguishing computer- and human-generated text is thus needed. Previous methods extract fluency features from a limited internal corpus and use them to identify the generated text. We have extended this approach to also estimate fluency using an enormous external corpus. We have also developed a method for extracting and distinguishing the noises characteristically created by a person or a machine. For example, people frequently use spoken noise words (2morrow, wanna, etc.) and misspelled ones (comin, hapy, etc.) while machines frequently generate incorrect expressions (such as untranslated phrases). A method combining these fluency and noise features was evaluated using 1000 original English messages and 1000 artificial English ones translated from Spanish. The results show that this combined method had the highest accuracy (80.35 ) and the lowest equal error rate (19.44 ) compared with one of state-of-the-art methods, which uses syntactic parser. Moreover, experiments using texts in other languages produced similar results, demonstrated that our proposed method works consistently across various languages.
  @cite_11: We show that it is possible to automatically detect machine translated text at sentence level from monolingual corpora, using text classification methods. We show further that the accuracy with which a learned classifier can detect text as machine translated is strongly correlated with the translation quality of the machine translation system that generated it. Finally, we offer a generic machine translation quality estimation technique based on this approach, which does not require reference sentences.
summary: To overcome the limitation of parsing-based approach, Arase and Zhou recommended another method @cite_0 based on fluency estimation. They mainly used @math -gram language model to estimate the fluency of continuous words. The restriction of this model is that it efficiently examines only on few continuous words, common in three. The authors reduced the deficiency by using sequential pattern mining to measure the fluency of non-continuous words. In-fluent patterns in human text are mined, such as ,'' ,'' that contrast with that in machine-generated text, for example, ,'' .'' There are two other reasonable combinations also aim to diminish the restriction of @math -gram model. The first combination @cite_3 extracted the specific noise words often used by a human, that is misspelled and reduction words, or by a machine, namely untranslated words. This combination, however, is only efficient in online social network in which contains a substantial number of such noises. The second combination @cite_11 focused on functional words abundantly occurring in machine-translated text. Additional features in the three combinations achieve non-high performances but these features effectively improve the overall performances when they are integrated with the original @math -gram model.