Papers by Aleem Khan
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. |
Defending Against Disinformation Attacks in Open-Domain Question Answering (2024.eacl-short)
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| Challenge: | Existing methods to defend against data poisoning attacks in open-domain question answering are lacking . |
| Approach: | They propose a method that uses query augmentation to find diverse passages that could answer the original question but are less likely to have been poisoned. |
| Outcome: | The proposed method provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts. |
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. |