Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards a Broad Coverage Named Entity Resource: A Data-Efficient
  Approach for Many Diverse Languages   Parallel corpora are ideal for extracting a multilingual named entity (MNE)
resource, i.e., a dataset of names translated into multiple languages. Prior
work on extracting MNE datasets from parallel corpora required resources such
as large monolingual corpora or word aligners that are unavailable or perform
poorly for underresourced languages. We present CLC-BN, a new method for
creating an MNE resource, and apply it to the Parallel Bible Corpus, a corpus
of more than 1000 languages. CLC-BN learns a neural transliteration model from
parallel-corpus statistics, without requiring any other bilingual resources,
word aligners, or seed data. Experimental results show that CLC-BN clearly
outperforms prior work. We release an MNE resource for 1340 languages and
demonstrate its effectiveness in two downstream tasks: knowledge graph
augmentation and bilingual lexicon induction.

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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: nucl-ex