We analyze whether pre-trained representations provide a greater benefit in-domain or out-of-domain, by comparing relative performance improvements on in-domain and out-ofdomain treebanks when augmenting the neural parsers with pre-trained language representations.
these aggregate F1 scores decompose along the structure of the tree, and are dominated by the short spans which make up the bulk of any treebank.