@article{TACL1199,
	author = {Liu, Jiangming  and Zhang, Yue },
	title = {In-Order Transition-based Constituent Parsing},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {5},
	year = {2017},
	keywords = {},
	abstract = {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.},
	issn = {2307-387X},
	url = {https://transacl.org/ojs/index.php/tacl/article/view/1199},
	pages = {413--424}
}