Automatic Detection of Machine Generated Text: A Critical Survey

Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan, V.S.


Abstract
Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.
Anthology ID:
2020.coling-main.208
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2296–2309
Language:
URL:
https://aclanthology.org/2020.coling-main.208
DOI:
10.18653/v1/2020.coling-main.208
Bibkey:
Cite (ACL):
Ganesh Jawahar, Muhammad Abdul-Mageed, and Laks Lakshmanan, V.S.. 2020. Automatic Detection of Machine Generated Text: A Critical Survey. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2296–2309, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Automatic Detection of Machine Generated Text: A Critical Survey (Jawahar et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.208.pdf
Code
 UBC-NLP/coling2020_machine_generated_text
Data
WebText