  Both bottom-up and top-down strategies have been used for neural
transition-based constituent parsing. The parsing strategies differ in terms of
the order in which they recognize productions in the derivation tree, where
bottom-up strategies and top-down strategies take post-order and pre-order
traversal over trees, respectively. Bottom-up parsers benefit from rich
features from readily built partial parses, but lack lookahead guidance in the
parsing process; top-down parsers benefit from non-local guidance for local
decisions, but rely on a strong encoder over the input to predict a constituent
hierarchy before its construction.To mitigate both issues, we propose a novel
parsing system based on in-order traversal over syntactic trees, designing a
set of transition actions to find a compromise between bottom-up constituent
information and top-down lookahead information. Based on stack-LSTM, our
psycholinguistically motivated constituent parsing system achieves 91.8 F1 on
WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised
reranking and 94.2 F1 with semi-supervised reranking, which are the best
results on the WSJ benchmark.
