Machine Translated Text Detection Through Text Similarity with Round-Trip Translation
Hoang-Quoc Nguyen-Son, Tran Thao, Seira Hidano, Ishita Gupta, Shinsaku Kiyomoto
Abstract
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%).
BibTeX
@inproceedings{nguyen-son-etal-2021-machine,
title = "Machine Translated Text Detection Through Text Similarity with Round-Trip Translation",
author = "Nguyen-Son, Hoang-Quoc and
Thao, Tran and
Hidano, Seira and
Gupta, Ishita and
Kiyomoto, Shinsaku",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.462/",
doi = "10.18653/v1/2021.naacl-main.462",
pages = "5792--5797"
}