Research
We present a new method LiST for efficient fine-tuning of large pre-trained language models (PLMs) in few-shot learning settings. LiST significantly improves over recent methods that adopt prompt fine-tuning using two key techniques. The first one is the use of…
This is the implementation of the paper Meta Self-training for Few-shot Neural Sequence Labeling. MetaST is short for meta-learning for self-training.
This is a source code release for a published research at NAACL 2021. Paper Title: MetaXL: Meta Representation Transformation for Low-resource Cross-Lingual Learning Paper Abstract: The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most…
State-of-the-art deep neural networks require large-scale labeled training data that is often either expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to…