Joint Multi-Label Attention Networks for Social Text Annotation

Hang Dong, Wei Wang, Kaizhu Huang, Frans Coenen


Abstract
We propose a novel attention network for document annotation with user-generated tags. The network is designed according to the human reading and annotation behaviour. Usually, users try to digest the title and obtain a rough idea about the topic first, and then read the content of the document. Present research shows that the title metadata could largely affect the social annotation. To better utilise this information, we design a framework that separates the title from the content of a document and apply a title-guided attention mechanism over each sentence in the content. We also propose two semantic-based loss regularisers that enforce the output of the network to conform to label semantics, i.e. similarity and subsumption. We analyse each part of the proposed system with two real-world open datasets on publication and question annotation. The integrated approach, Joint Multi-label Attention Network (JMAN), significantly outperformed the Bidirectional Gated Recurrent Unit (Bi-GRU) by around 13%-26% and the Hierarchical Attention Network (HAN) by around 4%-12% on both datasets, with around 10%-30% reduction of training time.
Anthology ID:
N19-1136
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1348–1354
Language:
URL:
https://aclanthology.org/N19-1136
DOI:
10.18653/v1/N19-1136
Bibkey:
Cite (ACL):
Hang Dong, Wei Wang, Kaizhu Huang, and Frans Coenen. 2019. Joint Multi-Label Attention Networks for Social Text Annotation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1348–1354, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Joint Multi-Label Attention Networks for Social Text Annotation (Dong et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1136.pdf
Poster:
 N19-1136.Poster.pdf