Summary of Stochastic Adversarial Networks For Multi-domain Text Classification, by Xu Wang and Yuan Wu
Stochastic Adversarial Networks for Multi-Domain Text Classification
by Xu Wang, Yuan Wu
First submitted to arxiv on: 28 May 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 This paper introduces a novel approach to multi-domain text classification (MDTC) called the Stochastic Adversarial Network (SAN). The SAN addresses the challenge of escalating model parameters by modeling the domain-specific feature extractor’s parameters as a multivariate Gaussian distribution, allowing for the generation of numerous extractors without increasing model size. The SAN also integrates domain label smoothing and robust pseudo-label regularization to stabilize adversarial training and refine feature discriminability. Experimental results on two leading MDTC benchmarks demonstrate the competitive edge of SAN against state-of-the-art methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes text classification better for many different kinds of texts. It uses a new way to train models that can handle lots of different types of texts, without getting too big or complicated. The new approach is called Stochastic Adversarial Network (SAN). SAN helps make the model more stable and able to tell apart different features in the text. This makes it better at classifying texts than other methods. The results show that SAN works well on two important tests. |
Keywords
» Artificial intelligence » Regularization » Text classification
