Exploring the Reformulation of NLP Tasks as Text Generation Tasks
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Date
2022-10-04
Authors
Hasan, Rasti
Journal Title
Journal ISSN
Volume Title
Publisher
University of Oregon
Abstract
In recent years, NLP classification tasks have been reformulated as text generation tasks in the form of text-to-text transformer-based models that achieve state-of-the-art performance by better utilizing pre-trained language models. This work provides a historical background, a taxonomy based on the output structures of these methods, an exploration of aspects of such models with several representative works, and discusses the current state and future of these models.
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Keywords
Generative Models, Natural Language Processing