Papers by Mitch Paul Mithun
Data and Model Distillation as a Solution for Domain-transferable Fact Verification (2021.naacl-main)
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| Challenge: | Neural networks depend heavily on lexicalized information, which transfers poorly between domains. |
| Approach: | They propose a method to delexicize lexicalized data and a model distillation technique to prevent aggressive data distillation. |
| Outcome: | The proposed method improves performance on lexicalized data and out of domain models. |
Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification (2021.emnlp-main)
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| Challenge: | Neural networks depend heavily on lexicalized information, which can be overfitted . this can be a problem in fact verification, which has important societal implications. |
| Approach: | They propose a knowledge distillation approach for fact verification using student models. |
| Outcome: | The proposed approach outperforms state-of-the-art classifiers on a training dataset and in supervised settings. |