Islam Nassar


2022

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Generate, Annotate, and Learn: NLP with Synthetic Text
Xuanli He | Islam Nassar | Jamie Kiros | Gholamreza Haffari | Mohammad Norouzi
Transactions of the Association for Computational Linguistics, Volume 10

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 take advantage of synthetic text within knowledge distillation, self-training, and few-shot learning applications. To generate high-quality task-specific text, we either fine-tune LMs on inputs from the task of interest, or prompt large LMs with few examples. We use the best available classifier to annotate synthetic text with soft pseudo labels for knowledge distillation and self-training, and use LMs to obtain hard labels for few-shot learning. We train new supervised models on the combination of labeled and pseudo-labeled data, which results in significant gains across several applications. We investigate key components of GAL and present theoretical and empirical arguments against the use of class-conditional LMs to generate synthetic labeled text instead of unlabeled text. GAL achieves new state-of-the-art knowledge distillation results for 6-layer transformers on the GLUE leaderboard.

2019

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Neural Versus Non-Neural Text Simplification: A Case Study
Islam Nassar | Michelle Ananda-Rajah | Gholamreza Haffari
Proceedings of the The 17th Annual Workshop of the Australasian Language Technology Association