Search Results for author:
Found 3 papers, 0 papers with code
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
In this paper we propose a novel data augmentation approach where guided outputs of a language generation model, e. g. GPT-2, when labeled, can improve the performance of text classifiers through an active learning process.
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation
To overcome this challenge, we present a novel approach where knowledge can be distilled from a teacher model to a student model through the generation of synthetic data.
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming
We propose a novel fine-tuning approach that prepends the rhyming word at the start of each lyric, which allows the critical rhyming decision to be made before the model commits to the content of the lyric (as during reverse language modeling), but maintains compatibility with the word order of regular PLMs as the lyric itself is still generated in left-to-right order.
