Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification

Ryo Masumura, Yusuke Shinohara, Ryuichiro Higashinaka, Yushi Aono


Abstract
This paper proposes an adversarial training method for the multi-task and multi-lingual joint modeling needed for utterance intent classification. In joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. This is achieved by introducing both language-specific networks shared among different tasks and task-specific networks shared among different languages. However, the shared networks are often specialized in majority tasks or languages, so performance degradation must be expected for some minor data sets. In order to improve the invariance of shared networks, the proposed method introduces both language-specific task adversarial networks and task-specific language adversarial networks; both are leveraged for purging the task or language dependencies of the shared networks. The effectiveness of the adversarial training proposal is demonstrated using Japanese and English data sets for three different utterance intent classification tasks.
Anthology ID:
D18-1064
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
633–639
Language:
URL:
https://aclanthology.org/D18-1064
DOI:
10.18653/v1/D18-1064
Bibkey:
Cite (ACL):
Ryo Masumura, Yusuke Shinohara, Ryuichiro Higashinaka, and Yushi Aono. 2018. Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 633–639, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification (Masumura et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1064.pdf
Video:
 https://vimeo.com/305212477