8 http://nlp.stanford.edu/projects/ glove/ High resource In the high-resource setting, we assume access to plentiful computational resources and massive amounts of out-of-domain data, which may be indirectly accessed through pretrained models.
we evaluate the performance of a Transformer-based ELMO (Peters et al., 2018b) and BERT, both (a) off-theshelf with frozen embeddings and (b) after semisupervised fine-tuning to both unlabeled and labeled in-domain data.