Cross-Domain Generalization of Neural Constituency Parsers
Table 6: Comparison of F1 scores for neural models with BERT pretraining to past state-of-the art results on transfer to the out-of-domain treebanks: (C+’15: Choe et al. 2015, L+’12: Le Roux et al. 2012).777Although the F1 scores obtained here are higher than the zero-shot transfer results of Joshi et al. (2018) on the Brown and Genia corpora due to the use of improved encoder (BERT) and decoder (self-attentive Chart and In-Order) models, we note the results are not directly comparable due to the use of different sections of the corpora for evaluation. EWT scores are averaged across the 3 SANCL’12 test sets, as reported by Petrov and McDonald (2012).
['[EMPTY]', 'prior work', 'Chart +BERT', 'In-Order +BERT']
Neural parsers generalize surprisingly well, and are able to draw benefits both from pre-trained language representations and structured output prediction. We note that these systems from prior work Choe et al. ; Le Roux et al. Encouragingly, parser improvements on the WSJ and CTB treebanks still transfer out-of-domain, indicating that improving results on these benchmarks may still continue to yield benefits in broader domains.