Furthermore, contextualized word representations learned from large-scale unlabeled texts under language model training loss Radford et al., 2018;Devlin et al., 2018), are proven to be extensively helpful for many NLP tasks including dependency parsing (Che et al., 2018;Kitaev and Klein, 2018).
parsing performance drops dramatically when processing texts that are different from the training data, known as the domain adaptation problem.