Abstract
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.