Papers by George Leung
Efficient Semi-supervised Consistency Training for Natural Language Understanding (2022.naacl-industry)
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| Challenge: | Manually labeled training data is expensive, noisy, and often scarce . semi-supervised learning methods can be used to improve model performance . |
| Approach: | They explore different methods for consistency training on unlabeled data . they use human paraphrasing, back-translation, and dropout to augment unlabed data. |
| Outcome: | The proposed methods outperform purely supervised learning on unlabeled data. |
Entity Contrastive Learning in a Large-Scale Virtual Assistant System (2023.acl-industry)
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| Challenge: | Named Entity Recognition (NER) tasks are a well-studied and fundamental task within Natural Language Understanding (NLU). |
| Approach: | They propose to incorporate entity contrastive learning into a virtual assistant system to improve NER models by clustering similar inputs closer together in a learned representation space. |
| Outcome: | The proposed model improves against a production baseline system that does not use contrastive learning. |