text: The next big step was the move towards using pretrained rep- 
  resentations of the distribution of words (called word embeddings) 
  in other (supervised) NLP tasks. These word vectors came from 
  systems such as word2vec [85] and GloVe [98] and later LSTM 
  models such as context2vec [82] and ELMo [99] and supported 
  state of the art performance on question answering, textual entail- 
  ment, semantic role labeling (SRL), coreference resolution, named 
  entity recognition (NER), and sentiment analysis, at first in Eng- 
  lish and later for other languages as well. While training the word 
  embeddings required a (relatively) large amount of data, it reduced 
  the amount of labeled data necessary for training on the various 
  supervised tasks. For example, [99] showed that a model trained 
  with ELMo reduced the necessary amount of training data needed 
  to achieve similar results on SRL compared to models without, as 
  shown in one instance where a model trained with ELMo reached 