Transcription of Improving Language Understanding by Generative Pre-Training
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Improving Language Understanding by Generative Pre-Training
Improving Language Understandingby Generative Pre-TrainingAlec Language Understanding comprises a wide range of diverse tasks suchas textual entailment, question answering, semantic similarity assessment, anddocument classification. Although large unlabeled text corpora are abundant,labeled data for learning these specific tasks is scarce, making it challenging fordiscriminatively trained models to perform adequately. We demonstrate that largegains on these tasks can be realized bygenerative pre-trainingof a Language modelon a diverse corpus of unlabeled text, followed bydiscriminative fine-tuningon eachspecific task.
We additionally found that including language modeling as an auxiliary objective to the fine-tuning helped learning by (a) improving generalization of the supervised model, and (b) accelerating convergence. This is in line with prior work [50, 43], who also observed improved performance with such an auxiliary objective.
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