Detecting Machine-Translated Text using Back Translation

Machine-translated text plays a crucial role in the communication of people using different languages. However, adversaries can use such text for malicious purposes such as plagiarism and fake review. The existing methods detected a machine-translated text only using the text's intrinsic content, but they are unsuitable for classifying the machine-translated and human-written texts with the same meanings. We have proposed a method to extract features used to distinguish machine/human text based on the similarity between the intrinsic text and its back-translation. The evaluation of detecting translated sentences with French shows that our method achieves 75.0% of both accuracy and F-score. It outperforms the existing methods whose the best accuracy is 62.8% and the F-score is 62.7%. The proposed method even detects more efficiently the back-translated text with 83.4% of accuracy, which is higher than 66.7% of the best previous accuracy. We also achieve similar results not only with F-score but also with similar experiments related to Japanese. Moreover, we prove that our detector can recognize both machine-translated and machine-back-translated texts without the language information which is used to generate these machine texts. It demonstrates the persistence of our method in various applications in both low- and rich-resource languages.Comment: INLG 2019, 9 page

FACTORS INFLUENCING TOURIST SATISFACTION WITH AGRITOURISM IN THE MEKONG DELTA, VIETNAM

The Mekong Delta, Vietnam possesses great potential and advantages for developing agritourism. To ensure the sustainable development of agritourism, enhance service quality, and improve tourist satisfaction are essential. This study aims to identify the factors influencing tourist satisfaction with agritourism in the Mekong Delta. Data were collected using a quota sampling method, with a sample size of 228 tourists who had visited and experienced agritourism in the Mekong Delta, Vietnam. A mixed-method approach combining qualitative and quantitative research was used to test research hypotheses. By applying structural equation modeling (SEM), the study demonstrated that local culture, natural landscape, novelty, assurance, tourism human resources, and perceived value positively influenced tourist satisfaction with agritourism in the Mekong Delta. Among these factors, the natural landscape had the most impact on tourist satisfaction with agritourism in the Mekong Delt

Group Scheduling for MultiChannel in OBS Networks

Group scheduling is a scheduling operation of optical burst switching networks in which the burst header packets arriving in each timeslot will schedule their following bursts simultaneously. There have been many proposals for group scheduling (such as OBS-GS, MWIS-OS and LGS), but they consider mainly to schedule the arriving bursts which have the same wavelength on an output data channel. Another suggestion is GreedyOPT which considers the group scheduling for multichannel with the support of full wavelength converters, but it is not optimal. This article proposes another approach of group scheduling which is more optimal and has a linear complexity

VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations

Adversarial attacks reveal serious flaws in deep learning models. More dangerously, these attacks preserve the original meaning and escape human recognition. Existing methods for detecting these attacks need to be trained using original/adversarial data. In this paper, we propose detection without training by voting on hard labels from predictions of transformations, namely, VoteTRANS. Specifically, VoteTRANS detects adversarial text by comparing the hard labels of input text and its transformation. The evaluation demonstrates that VoteTRANS effectively detects adversarial text across various state-of-the-art attacks, models, and datasets.Comment: Findings of ACL 2023 (long paper

Identifying Computer-Translated Paragraphs using Coherence Features

We have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences. We conducted an experiment with a parallel German corpus containing 2000 human-created and 2000 machine-translated paragraphs. The result showed that our method achieved the best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is compared with previous methods on various computer-generated text including translation and paper generation (best accuracy = 67.9%, equal error rate = 32.0%). Experiments on Dutch, another rich resource language, and a low resource one (Japanese) attained similar performances. It demonstrated the efficiency of the coherence features at distinguishing computer-translated from human-created paragraphs on diverse languages.Comment: 9 pages, PACLIC 201