In-Order Transition-based Constituent Parsing (Liu et al., 2017)

Shift-reduce constituency parsing incrementally builds the parse either bottom-up or top-down. The difference is whether a non-terminal is placed on the stack before or after the words that it spans. This corresponds to two forms of depth-first traversal of the tree: pre-order or post-order.

The idea in this paper is to do an in-order traversal, which in a binary tree means traversing the left child of a node, then the node, then its right child. In this context that means putting the non-terminal symbol on the stack after the first word it spans, but before the rest. The model follows the stack-LSTM approach of Dyer et al., with non-terminals always fed into the LSTM first during composition, regardless of where it was inserted into the stack.

This leads to a 0.5 F1 gain on standard parsing metrics, with no hyperparameter tuning. High-level error analysis seems to show it just does better everywhere. I wonder whether further gains could be realised with a label-sensitive ordering.



	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},
	issn = {2307-387X},
	url = {},
	pages = {413--424}


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