  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 in-order
linearization instead. Based on this observation, we implement an enriched
in-order 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-the-art transition-based parsers, thus showing that
sequence-to-sequence models can match them, not only in accuracy, but also in
speed.
