@inproceedings{zhong-etal-2020-neural,
title = "Neural Deepfake Detection with Factual Structure of Text",
author = "Zhong, Wanjun and
Tang, Duyu and
Xu, Zenan and
Wang, Ruize and
Duan, Nan and
Zhou, Ming and
Wang, Jiahai and
Yin, Jian",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.193",
doi = "10.18653/v1/2020.emnlp-main.193",
pages = "2461--2470",
abstract = "Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text.",
}
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<abstract>Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text.</abstract>
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%0 Conference Proceedings
%T Neural Deepfake Detection with Factual Structure of Text
%A Zhong, Wanjun
%A Tang, Duyu
%A Xu, Zenan
%A Wang, Ruize
%A Duan, Nan
%A Zhou, Ming
%A Wang, Jiahai
%A Yin, Jian
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhong-etal-2020-neural
%X Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text.
%R 10.18653/v1/2020.emnlp-main.193
%U https://aclanthology.org/2020.emnlp-main.193
%U https://doi.org/10.18653/v1/2020.emnlp-main.193
%P 2461-2470
Markdown (Informal)
[Neural Deepfake Detection with Factual Structure of Text](https://aclanthology.org/2020.emnlp-main.193) (Zhong et al., EMNLP 2020)
ACL
- Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, and Jian Yin. 2020. Neural Deepfake Detection with Factual Structure of Text. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2461–2470, Online. Association for Computational Linguistics.