Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low-Resource Sequence Labeling via Unsupervised Multilingual
  Contextualized Representations   Previous work on cross-lingual sequence labeling tasks either requires
parallel data or bridges the two languages through word-byword matching. Such
requirements and assumptions are infeasible for most languages, especially for
languages with large linguistic distances, e.g., English and Chinese. In this
work, we propose a Multilingual Language Model with deep semantic Alignment
(MLMA) to generate language-independent representations for cross-lingual
sequence labeling. Our methods require only monolingual corpora with no
bilingual resources at all and take advantage of deep contextualized
representations. Experimental results show that our approach achieves new
state-of-the-art NER and POS performance across European languages, and is also
effective on distant language pairs such as English and Chinese.

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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: mtrl-th