Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence
  Representations   We investigate whether off-the-shelf deep bidirectional sentence
representations trained on a massively multilingual corpus (multilingual BERT)
enable the development of an unsupervised universal dependency parser. This
approach only leverages a mix of monolingual corpora in many languages and does
not require any translation data making it applicable to low-resource
languages. In our experiments we outperform the best CoNLL 2018
language-specific systems in all of the shared task's six truly low-resource
languages while using a single system. However, we also find that (i) parsing
accuracy still varies dramatically when changing the training languages and
(ii) in some target languages zero-shot transfer fails under all tested
conditions, raising concerns on the 'universality' of the whole approach.

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The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat