Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing

Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences. Top-down tree linearizations, which can be based on brackets or shift-reduce actions, have achieved the best accuracy to date. In this paper, we show that these results can be improved by using an inorder linearization instead. Based on this observation, we implement an enriched inorder shift-reduce linearization inspired by Vinyals et al. ( 2015 )'s approach, achieving the best accuracy to date on the English PTB dataset among fully-supervised single-model sequence-to-sequence constituent parsers. Finally, we apply deterministic attention mechanisms to match the speed of state-of-theart transition-based parsers, thus showing that sequence-to-sequence models can match them, not only in accuracy, but also in speed.