Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-Lingual Semantic Role Labeling with High-Quality Translated
  Training Corpus   Many efforts of research are devoted to semantic role labeling (SRL) which is
crucial for natural language understanding. Supervised approaches have achieved
impressing performances when large-scale corpora are available for
resource-rich languages such as English. While for the low-resource languages
with no annotated SRL dataset, it is still challenging to obtain competitive
performances. Cross-lingual SRL is one promising way to address the problem,
which has achieved great advances with the help of model transferring and
annotation projection. In this paper, we propose a novel alternative based on
corpus translation, constructing high-quality training datasets for the target
languages from the source gold-standard SRL annotations. Experimental results
on Universal Proposition Bank show that the translation-based method is highly
effective, and the automatic pseudo datasets can improve the target-language
SRL performances significantly.

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The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: eess
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th