Ishita Gupta


2021

pdf bib
Machine Translated Text Detection Through Text Similarity with Round-Trip Translation
Hoang-Quoc Nguyen-Son | Tran Thao | Seira Hidano | Ishita Gupta | Shinsaku Kiyomoto
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Translated texts have been used for malicious purposes, i.e., plagiarism or fake reviews. Existing detectors have been built around a specific translator (e.g., Google) but fail to detect a translated text from a strange translator. If we use the same translator, the translated text is similar to its round-trip translation, which is when text is translated into another language and translated back into the original language. However, a round-trip translated text is significantly different from the original text or a translated text using a strange translator. Hence, we propose a detector using text similarity with round-trip translation (TSRT). TSRT achieves 86.9% accuracy in detecting a translated text from a strange translator. It outperforms existing detectors (77.9%) and human recognition (53.3%).