  Neural parsers obtain state-of-the-art results on benchmark treebanks for
constituency parsing -- but to what degree do they generalize to other domains?
We present three results about the generalization of neural parsers in a
zero-shot setting: training on trees from one corpus and evaluating on
out-of-domain corpora. First, neural and non-neural parsers generalize
comparably to new domains. Second, incorporating pre-trained encoder
representations into neural parsers substantially improves their performance
across all domains, but does not give a larger relative improvement for
out-of-domain treebanks. Finally, despite the rich input representations they
learn, neural parsers still benefit from structured output prediction of output
trees, yielding higher exact match accuracy and stronger generalization both to
larger text spans and to out-of-domain corpora. We analyze generalization on
English and Chinese corpora, and in the process obtain state-of-the-art parsing
results for the Brown, Genia, and English Web treebanks.
