Papers by Neil Mallinar
Unsupervised Adaptation of Question Answering Systems via Generative Self-training (2020.emnlp-main)
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| Challenge: | Supervised self-training methods have transformed applied machine learning . however, adapting to target data has received little attention . |
| Approach: | They propose a method to generate synthetic QA pairs for unsupervised self adaptation . they use massive amounts of data to simulate self-supervised tasks . |
| Outcome: | The proposed method improves QA systems significantly by using less data and training computation than existing augmentation approaches. |