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cs.LG2021★ 1 cited
Generate, Annotate, and Learn: NLP with Synthetic Text
Xuanli He, Islam Nassar, Jamie Kiros +2
This paper studies the use of language models as a source of synthetic unlabeled text for NLP. We formulate a general framework called ``generate, annotate, and learn (GAL)'' to ta…
cs.CV2021
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
Islam Nassar, Samitha Herath, Ehsan Abbasnejad +2
Pseudo-labeling is a key component in semi-supervised learning (SSL). It relies on iteratively using the model to generate artificial labels for the unlabeled data to train against…