| Paper ID | HLT-14.6 |
| Paper Title |
Mixup Regularized Adversarial Networks for Multi-Domain Text Classification |
| Authors |
Yuan Wu, Carleton University, Canada; Diana Inkpen, University of Ottawa, Canada; Ahmed El-Roby, Carleton University, Canada |
| Session | HLT-14: Language Representations |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Human Language Technology: [HLT-MLMD] Machine Learning Methods for Language |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Using the shared-private paradigm and adversarial training can significantly improve the performance of multi-domain text classification (MDTC) models. However, there are two issues for the existing methods: First, instances from the multiple domains are not sufficient for domain-invariant feature extraction. Second, aligning on the marginal distributions may lead to a fatal mismatch. In this paper, we propose mixup regularized adversarial networks (MRANs) to address these two issues. More specifically, the domain and category mixup regularizations are introduced to enrich the intrinsic features in the shared latent space and enforce consistent predictions in-between training instances such that the learned features can be more domain-invariant and discriminative. We conduct experiments on two benchmarks: The Amazon review dataset and the FDU-MTL dataset. Our approach on these two datasets yields average accuracies of 87.64\% and 89.0\% respectively, outperforming all relevant baselines. |
