Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Neural Parsing by Disentangling Model Combination and
  Reranking Effects   Recent work has proposed several generative neural models for constituency
parsing that achieve state-of-the-art results. Since direct search in these
generative models is difficult, they have primarily been used to rescore
candidate outputs from base parsers in which decoding is more straightforward.
We first present an algorithm for direct search in these generative models. We
then demonstrate that the rescoring results are at least partly due to implicit
model combination rather than reranking effects. Finally, we show that explicit
model combination can improve performance even further, resulting in new
state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data
and 94.66 F1 when using external data.

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The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: eess
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex