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Adversarial Domain Adaptation Network for Semantic Role Classification

Haitong YANG, Guangyou ZHOU, Tingting HE, Maoxi LI

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Summary :

In this paper, we study domain adaptation of semantic role classification. Most systems utilize the supervised method for semantic role classification. But, these methods often suffer severe performance drops on out-of-domain test data. The reason for the performance drops is that there are giant feature differences between source and target domain. This paper proposes a framework called Adversarial Domain Adaption Network (ADAN) to relieve domain adaption of semantic role classification. The idea behind our method is that the proposed framework can derive domain-invariant features via adversarial learning and narrow down the gap between source and target feature space. To evaluate our method, we conduct experiments on English portion in the CoNLL 2009 shared task. Experimental results show that our method can largely reduce the performance drop on out-of-domain test data.

Publication
IEICE TRANSACTIONS on Information Vol.E102-D No.12 pp.2587-2594
Publication Date
2019/12/01
Publicized
2019/09/02
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDP7087
Type of Manuscript
PAPER
Category
Natural Language Processing

Authors

Haitong YANG
  China Central Normal University
Guangyou ZHOU
  China Central Normal University
Tingting HE
  China Central Normal University
Maoxi LI
  Jiangxi Normal University

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