Who Wrote When? Author Diarization in Social Media Discussions (2024.findings-emnlp)
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| Challenge: | Existing approaches for author diarization are unable to detect stylistic shifts in a text . |
| Approach: | They propose a framework that integrates pre-trained neural representations of writing style with author-conditional encoder-decoder diarization. |
| Outcome: | The proposed framework is able to attribute comments in online discussions to individual authors. |
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| Challenge: | Existing annotated corpus of Reddit comments is limited by available annotation methods. |
| Approach: | They propose a Bayesian approach that directly represents authors’ propensities to be sarcastic and a dense embedding approach that can learn interactions between the author and the text. |
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Latent Space Interpretation for Stylistic Analysis and Explainable Authorship Attribution (2025.coling-main)
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| Challenge: | Recent authorship attribution methods learn authorship representations of text in a latent, uninterpretable space, which hinders their usability in real-world applications. |
| Approach: | They propose a method for interpreting latent authorship representations by identifying representative points in the latent space and leveraging large language models to generate informative natural language descriptions of the writing style associated with each point. |
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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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Layered Insights: Generalizable Analysis of Human Authorial Style by Leveraging All Transformer Layers (2025.emnlp-main)
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| Challenge: | Existing approaches to authorship attribution model only learn from the output layer of pre-trained transformers, ignoring representations learned at other layers. |
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Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
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| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
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Detecting, Generating, and Evaluating in the Writing Style of Different Authors (2025.naacl-srw)
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| Challenge: | In recent years, stylometry has been investigated in many different fields. |
| Approach: | They propose to use sentences from different books to generate and evaluate stylistic texts according to the authors' writing styles. |
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Handling Normalization Issues for Part-of-Speech Tagging of Online Conversational Text (L18-1)
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Géraldine Damnati, Jeremy Auguste, Alexis Nasr, Delphine Charlet, Johannes Heinecke, Frédéric Béchet
| Challenge: | a new approach to POS tagging noisy user generated text is proposed . word embeddings are trained on a noisy corpus to address both normalization and POS. |
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Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI (2026.acl-long)
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| Challenge: | Existing methods struggle with content-style entanglement, leading to poor generalization across domains. |
| Approach: | They propose an explanation-by-design framework that explicitly disentangles style from content through architectural separation-by design. |
| Outcome: | The proposed framework disentangles style from content through architectural separation-by-design. |
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. |
| Approach: | They propose a framework that creates authorship representation with reduced reliance on topic-specific information to handle a large number of unseen authors and topics. |
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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. |
| Approach: | They propose to use neural network-based language models to generate realistic texts . they investigate the authorship attribution problem in three versions of a text . |
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