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2023.acl-long.92.pdf
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🔗 Source: aclanthology.org
📊 Size: 2.24 MB
📄 Pages: 19 pages
⬇️ Downloads: 318

Summary

Researchers propose a new self-training framework, Domain Adversarial Learning Enhanced Meta Self-Training Framework (DaMSTF), for domain adaptation. DaMSTF involves meta-learning to estimate the importance of each pseudo instance, reducing label noise and preserving hard examples. A meta constructor constructs a high-quality meta validation set, and domain adversarial learning addresses the training guidance vanishment problem. DaMSTF improves BERT's performance by an average of nearly 4% on cross-domain sentiment classification tasks.

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Description

Researchers propose a new self-training framework, Domain Adversarial Learning Enhanced Meta Self-Training Framework (DaMSTF), for domain adaptation.

Technical Information

  • File Format: PDF
  • File Size: 2.24 MB
  • Pages: 19
  • Language: EN
  • Total Downloads: 318
  • Last Updated: 2 hours ago

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