Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Multi-Criteria Learning for Chinese Word Segmentation   Different linguistic perspectives causes many diverse segmentation criteria
for Chinese word segmentation (CWS). Most existing methods focus on improve the
performance for each single criterion. However, it is interesting to exploit
these different criteria and mining their common underlying knowledge. In this
paper, we propose adversarial multi-criteria learning for CWS by integrating
shared knowledge from multiple heterogeneous segmentation criteria. Experiments
on eight corpora with heterogeneous segmentation criteria show that the
performance of each corpus obtains a significant improvement, compared to
single-criterion learning. Source codes of this paper are available on Github.

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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: math
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys