Year: 2021
Type: conference-paper
Abstract: Some NLP tasks can be solved in a fully unsupervised fashion by providing a pretrained language model with "task descriptions" in natural language (e.g., Radford et al., 2019).While this approach underperforms its supervised counterpart, we show in this work that the two ideas can be combined: We introduce Pattern-Exploiting Training (PET), a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task.These phrases are then used to assign soft labels to a large set of unlabeled examples.Finally, standard supervised training is performed on the resulting training set.For several tasks and languages, PET outperforms supervised training and strong semi-supervised approaches in lowresource settings by a large margin. 1
Authors: Timo Schick, Hinrich Schütze
Institution: Ludwig-Maximilians-Universität München
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: H2020 European Research Council
OpenAlex ID: W3002104146