mlsanityData to 14 Oct 2026

What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training

arXiv 1805.06088 ↗First posted 16 May 2018cs.CLcited by 17

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Abstract (arXiv)

Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (in domain) and dissimilar (out of domain) instances to those seen in training. This requires learning an underlying task, while not learning irrelevant signals and biases specific to individual domains. We propose a novel method to optimise both in- and out-of-domain accuracy based on joint learning of a structured neural model with domain-specific and domain-general components, coupled with adversarial training for domain. Evaluating on multi-domain language identification and multi-domain sentiment analysis, we show substantial improvements over standard domain adaptation techniques, and domain-adversarial training.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2018.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 1; 2024: 2; 2025: 2.

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  1. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  2. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 98
  3. 2021Diverse Adversaries for Mitigating Bias in Trainingcited by 48
  4. 2021Balancing out Bias: Achieving Fairness Through Balanced Trainingcited by 34
  5. 2021UDALM: Unsupervised Domain Adaptation through Language Modelingcited by 33
  6. 2021Cross-Domain Label-Adaptive Stance Detectioncited by 30
  7. 2021Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Trainingcited by 19
  8. 2023Language Variety Identification with True Labelscited by 12
  9. 2022Towards Equal Opportunity Fairness through Adversarial Learningcited by 8
  10. 2022Explainable Misinformation Detection Across Multiple Social Media Platformscited by 5
  11. 2021Cross-Lingual Transfer Learning for Statistical Type Inferencecited by 2
  12. 2021ASAT: Adaptively Scaled Adversarial Training in Time Seriescited by 2
5 more
  1. 2024Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generationcited by 2
  2. 2025Multi-Domain Explainability of Preferencescited by 2
  3. 2025JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learningcited by 1
  4. 2022Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysiscited by 0
  5. 2024Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classificationcited by 0