668 papers with code • 1 benchmarks • 1 datasets
These leaderboards are used to track progress in text-classification
Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.
We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory.
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.
In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.