Papers by Marcello Hasegawa
Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels (2021.naacl-main)
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Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
| Challenge: | Neural language models have a high capacity for memorization of training samples . however, this can cause privacy degradation and disparate impact on subgroups of users . |
| Approach: | They propose two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy. |
| Outcome: | The proposed methods have favorable utility-privacy trade-off, faster training and uniform treatment of under-represented subgroups. |
Smart To-Do: Automatic Generation of To-Do Items from Emails (2020.acl-main)
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| Challenge: | Using neural text generation, we generate To-Do items from emails where the sender has promised to perform an action. |
| Approach: | They propose a task and dataset for automatically generating To-Do items from emails where the sender has promised to perform an action. |
| Outcome: | The proposed task obtains BLEU and ROUGE scores of 0.23 and 0.63 for the task. |