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. |
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| Challenge: | Existing approaches to authorship attribution rely on individual's writing style and/or preferred topics. |
| Approach: | They analyse four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. |
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Topic-Regularized Authorship Representation Learning (2022.emnlp-main)
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| Challenge: | Existing techniques for authorship attribution have focused on out-of-distribution in topics or authors. |
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What represents “style” in authorship attribution? (C18-1)
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| Challenge: | Authorship attribution uses all information representing content and style whereas stylometry is robust in cross-domain settings. |
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Can Authorship Representation Learning Capture Stylistic Features? (2023.tacl-1)
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Andrew Wang, Cristina Aggazzotti, Rebecca Kotula, Rafael Rivera Soto, Marcus Bishop, Nicholas Andrews
| Challenge: | Existing methods to disentangle an author's style from the content of their writing are limited by the reliance on human labels and the narrow focus of stylistic distinctions. |
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Mode Effects’ Challenge to Authorship Attribution (2021.eacl-main)
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| Challenge: | Existing studies on authorship attribution have shown that authorial style changes with respect to sentence length, word use, readability, and certain part-of-speech ratios. |
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Open-World Authorship Attribution (2025.findings-acl)
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| Challenge: | Existing benchmarks for large language models do not evaluate their performance in academic research . authors aim to identify authors from anonymous text without additional information . |
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Authorless Topic Models: Biasing Models Away from Known Structure (C18-1)
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| Challenge: | a recent study shows that topic models that highlight differences in authors are often not accurate . authors show that subsampling words that are highly correlated with metadata can reduce topic-metadata correlation . |
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Authorship Attribution for Neural Text Generation (2020.emnlp-main)
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| Challenge: | Recent advances in deep learning have enabled the generation of realistic artifacts . however, the qualities of texts generated by these models are better, often confusing classifiers if they are not real. |
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Adversarial Authorship Attribution for Deobfuscation (2022.acl-long)
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| Challenge: | Existing authorship attribution approaches do not consider adversarial threat model . authors show adversarially trained authorship attributors can degrade effectiveness of existing obfuscators from 20-30% to 5-10% . |
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Whodunit? Learning to Contrast for Authorship Attribution (2022.aacl-main)
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| Challenge: | Existing approaches to authorship attribution are dataset-dependent and yield inconsistent performance across corpora. |
| Approach: | They propose to fine-tune pre-trained generic language representations with a contrastive objective to learn author-specific representations by identifying clusters of authors. |
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