Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Stacking Networks for Low-Resource Chinese Word Segmentation with
  Transfer Learning   In recent years, neural networks have proven to be effective in Chinese word
segmentation. However, this promising performance relies on large-scale
training data. Neural networks with conventional architectures cannot achieve
the desired results in low-resource datasets due to the lack of labelled
training data. In this paper, we propose a deep stacking framework to improve
the performance on word segmentation tasks with insufficient data by
integrating datasets from diverse domains. Our framework consists of two parts,
domain-based models and deep stacking networks. The domain-based models are
used to learn knowledge from different datasets. The deep stacking networks are
designed to integrate domain-based models. To reduce model conflicts, we
innovatively add communication paths among models and design various structures
of deep stacking networks, including Gaussian-based Stacking Networks,
Concatenate-based Stacking Networks, Sequence-based Stacking Networks and
Tree-based Stacking Networks. We conduct experiments on six low-resource
datasets from various domains. Our proposed framework shows significant
performance improvements on all datasets compared with several strong
baselines.

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The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th