Papers by Marcus Bishop
Learning Invariant Representations of Social Media Users (D19-1)
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| Challenge: | Existing methods for learning to compare social media users fail to generalize to new users or even to previously known users. |
| Approach: | They propose a procedure to learn a mapping from short episodes of user activity to a vector space in which the distance between points captures the similarity of the corresponding users’ invariant features. |
| Outcome: | The proposed procedure can be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. |
Learning Universal Authorship Representations (2021.emnlp-main)
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Rafael A. Rivera-Soto, Olivia Elizabeth Miano, Juanita Ordonez, Barry Y. Chen, Aleem Khan, Marcus Bishop, Nicholas Andrews
| Challenge: | authorship verification has traditionally relied on modeling stylometric linguistic properties . but neural methods introduce a tradeoff: they obviate the need for manual feature design . |
| Approach: | They propose to use domain-specific features to improve authorship representations . they propose to study Amazon reviews, fanfiction short stories, and Reddit comments . |
| Outcome: | The proposed methods outperform existing methods in large-scale authorship verification scenarios. |
A Deep Metric Learning Approach to Account Linking (2021.naacl-main)
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| Challenge: | Existing methods to identify abusive content may fail to adapt to new trends, and individual posts may fail . |
| Approach: | They propose a method that embeds variable-sized samples of user activity into a vector space, where samples by the same author map to nearby points. |
| Outcome: | The proposed model outperforms several competitive baselines under a new evaluation framework modeled after established benchmarks in other domains. |
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. |
| Approach: | They propose to use a surrogate task to learn authorship representations that are sensitive to writing style and to validate their hypothesis . |
| Outcome: | The proposed representations are sensitive to writing style and may be robust to topic drift over time. |