Papers by Benjamin C. M. Fung
The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution (2021.findings-emnlp)
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| Challenge: | Autorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors. |
| Approach: | They propose a topic confusion task where they switch the author-topic configuration between training and testing sets and propose attribution errors that are caused by the topic shift and by the features’ inability to capture the writing styles. |
| Outcome: | The proposed task combines author-topic configuration with other features to lower topic confusion and higher attribution accuracy. |
ER-AE: Differentially Private Text Generation for Authorship Anonymization (2021.naacl-main)
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| Challenge: | Recent studies on privacy protection for textual data focus on removing explicit sensitive identifiers without considering the author's writing style. |
| Approach: | They propose a text generation model with an exponential mechanism for authorship anonymization that augments the semantic information through a REINFORCE training reward function. |
| Outcome: | The proposed model outperforms the state-of-the-art on semantic preservation, authorship obfuscation, and stylometric transformation on the real-life peer reviews and Yelp review datasets. |
A Multifaceted Framework to Evaluate Evasion, Content Preservation, and Misattribution in Authorship Obfuscation Techniques (2022.emnlp-main)
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| Challenge: | Authorship obfuscation techniques are often evaluated based on their ability to hide the author’s identity (evasion) while preserving the content of the original text. |
| Approach: | They propose to evaluate authorship obfuscation techniques on detection evasion and content preservation using competitive identification techniques in real-life scenarios. |
| Outcome: | The proposed method reveals key weaknesses in state-of-the-art obfuscation techniques and surprisingly competitive effectiveness from a back-translation baseline in all evaluation aspects. |