Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Task Cross-Lingual Sequence Tagging from Scratch   We present a deep hierarchical recurrent neural network for sequence tagging.
Given a sequence of words, our model employs deep gated recurrent units on both
character and word levels to encode morphology and context information, and
applies a conditional random field layer to predict the tags. Our model is task
independent, language independent, and feature engineering free. We further
extend our model to multi-task and cross-lingual joint training by sharing the
architecture and parameters. Our model achieves state-of-the-art results in
multiple languages on several benchmark tasks including POS tagging, chunking,
and NER. We also demonstrate that multi-task and cross-lingual joint training
can improve the performance in various cases.

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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph