Papers by Ansel MacLaughlin
Content-based Models of Quotation (2021.eacl-main)
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| Challenge: | Prior work has focused on manual feature engineering and development of frameworks to test factors that influence quotability. |
| Approach: | They propose to use quotability identification as a passage ranking problem to evaluate models' performance . they use five datasets that span multiple languages and genres of literature . |
| Outcome: | The proposed model outperforms the existing model on five datasets that span multiple languages and genres of literature. |
Recovering Lexically and Semantically Reused Texts (2021.starsem-1)
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| Challenge: | Writers often repurpose material from existing texts when composing new documents. |
| Approach: | They propose to use local text reuse detection to detect localized regions of lexically or semantically similar text embedded in otherwise unrelated material. |
| Outcome: | The proposed methods perform better on three LTRD tasks, detecting plagiarism, modeling journalists’ use of press releases, and identifying scientists’ citation of earlier papers. |
Federated Learning with Noisy User Feedback (2022.naacl-main)
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Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta
| Challenge: | Artificial Intelligence (AI) and Machine Learning (ML) systems are becoming more popular and are causing concerns over user privacy. |
| Approach: | They propose a method for training ML models using positive and negative user feedback and a framework to extract labels on edge to make FL viable. |
| Outcome: | The proposed method improves significantly over a self-training baseline, achieving performance closer to models trained with full supervision. |