In the past year, the field of Natural Language Processing (NLP) has seen the rise of pretrained language models such as as ELMo (Peters et al., 2018) , ULMFiT (Howard and Ruder, 2018) and <cite>BERT</cite> (<cite>Devlin et al., 2019</cite>) . <cite>These approaches</cite> train a deep-learning language model on large volumes of unlabeled text, which is subsequently fine-tuned for particular NLP tasks.