Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data

Zixuan Ke, Mohammad Kachuee, Sungjin Lee


Abstract
In many real-world machine learning applications, samples belong to a set of domains e.g., for product reviews each review belongs to a product category. In this paper, we study multi-domain imbalanced learning (MIL), the scenario that there is imbalance not only in classes but also in domains. In the MIL setting, different domains exhibit different patterns and there is a varying degree of similarity and divergence among domains posing opportunities and challenges for transfer learning especially when faced with limited or insufficient training data. We propose a novel domain-aware contrastive knowledge transfer method called DCMI to (1) identify the shared domain knowledge to encourage positive transfer among similar domains (in particular from head domains to tail domains); (2) isolate the domain-specific knowledge to minimize the negative transfer from dissimilar domains. We evaluated the performance of DCMI on three different datasets showing significant improvements in different MIL scenarios.
Anthology ID:
2022.wassa-1.3
Volume:
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Jeremy Barnes, Orphée De Clercq, Valentin Barriere, Shabnam Tafreshi, Sawsan Alqahtani, João Sedoc, Roman Klinger, Alexandra Balahur
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
25–36
Language:
URL:
https://aclanthology.org/2022.wassa-1.3
DOI:
10.18653/v1/2022.wassa-1.3
Bibkey:
Cite (ACL):
Zixuan Ke, Mohammad Kachuee, and Sungjin Lee. 2022. Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data. In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, pages 25–36, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced Data (Ke et al., WASSA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.wassa-1.3.pdf
Video:
 https://aclanthology.org/2022.wassa-1.3.mp4
Data
LIAR