Summary of Margin Discrepancy-based Adversarial Training For Multi-domain Text Classification, by Yuan Wu
Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification
by Yuan Wu
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to multi-domain text classification (MDTC) by decomposing the task into multiple domain adaptation tasks and establishing a new generalization bound based on Rademacher complexity. The proposed method, margin discrepancy-based adversarial training (MDAT), is designed to tackle the absence of theoretical guarantees in existing MDTC algorithms. Experimental results demonstrate that MDAT surpasses state-of-the-art baselines on two MDTC benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how we can improve text classification by using information from other related domains. The researchers developed a new way to analyze this problem and came up with a better approach called margin discrepancy-based adversarial training (MDAT). They tested their method and found that it works even better than the best methods currently available. |
Keywords
* Artificial intelligence * Domain adaptation * Generalization * Text classification
