Using Similarity Measures to Select Pretraining Data for NER (1904.00585v2)

Abstract: Word vectors and LLMs (LMs) pretrained on a large amount of unlabelled data can dramatically improve various NLP tasks. However, the measure and impact of similarity between pretraining data and target task data are left to intuition. We propose three cost-effective measures to quantify different aspects of similarity between source pretraining and target task data. We demonstrate that these measures are good predictors of the usefulness of pretrained models for Named Entity Recognition (NER) over 30 data pairs. Results also suggest that pretrained LMs are more effective and more predictable than pretrained word vectors, but pretrained word vectors are better when pretraining data is dissimilar.