Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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 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.

19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an