Multinomial Adversarial Networks for Multi-Domain Text Classification

Xilun Chen, Claire Cardie


Abstract
Many text classification tasks are known to be highly domain-dependent. Unfortunately, the availability of training data can vary drastically across domains. Worse still, for some domains there may not be any annotated data at all. In this work, we propose a multinomial adversarial network (MAN) to tackle this real-world problem of multi-domain text classification (MDTC) in which labeled data may exist for multiple domains, but in insufficient amounts to train effective classifiers for one or more of the domains. We provide theoretical justifications for the MAN framework, proving that different instances of MANs are essentially minimizers of various f-divergence metrics (Ali and Silvey, 1966) among multiple probability distributions. MANs are thus a theoretically sound generalization of traditional adversarial networks that discriminate over two distributions. More specifically, for the MDTC task, MAN learns features that are invariant across multiple domains by resorting to its ability to reduce the divergence among the feature distributions of each domain. We present experimental results showing that MANs significantly outperform the prior art on the MDTC task. We also show that MANs achieve state-of-the-art performance for domains with no labeled data.
Anthology ID:
N18-1111
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1226–1240
Language:
URL:
https://aclanthology.org/N18-1111
DOI:
10.18653/v1/N18-1111
Bibkey:
Cite (ACL):
Xilun Chen and Claire Cardie. 2018. Multinomial Adversarial Networks for Multi-Domain Text Classification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1226–1240, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Multinomial Adversarial Networks for Multi-Domain Text Classification (Chen & Cardie, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1111.pdf
Software:
 N18-1111.Software.tgz
Code
 ccsasuke/man