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Neural Unsupervised Domain Adaptation in NLP---A Survey

arXiv 2006.00632 ↗First posted 31 May 2020cs.CLcited by 142

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Abstract (arXiv)

Deep neural networks excel at learning from labeled data and achieve state-of-the-art resultson a wide array of Natural Language Processing tasks. In contrast, learning from unlabeled data, especially under domain shift, remains a challenge. Motivated by the latest advances, in this survey we review neural unsupervised domain adaptation techniques which do not require labeled target domain data. This is a more challenging yet a more widely applicable setup. We outline methods, from early traditional non-neural methods to pre-trained model transfer. We also revisit the notion of domain, and we uncover a bias in the type of Natural Language Processing tasks which received most attention. Lastly, we outline future directions, particularly the broader need for out-of-distribution generalization of future NLP.

Key facts. Cited by 142 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2020.

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Citing papers per year: 2021: 32; 2022: 34; 2023: 43; 2024: 16; 2025: 13; 2026: 4.

142 papers.

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  1. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 337
  2. 2021DEMix Layers: Disentangling Domains for Modular Language Modelingcited by 130
  3. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  4. 2023Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluationscited by 96
  5. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  6. 2021Domain Adaptation for Time Series Forecasting via Attention Sharingcited by 68
  7. 2021PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domainscited by 61
  8. 2023Domain Adaptation for Time Series Under Feature and Label Shiftscited by 60
  9. 2021Incorporating Domain Knowledge into Deep Neural Networkscited by 56
  10. 2023Patton: Language Model Pretraining on Text-Rich Networkscited by 49
  11. 2022Revisiting Transformer-based Models for Long Document Classificationcited by 48
  12. 2021Named Entity Recognition and Classification on Historical Documents: A Surveycited by 47
38 more
  1. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  2. 2022Memorization in NLP Fine-tuning Methodscited by 43
  3. 2023ConDA: Contrastive Domain Adaptation for AI-generated Text Detectioncited by 37
  4. 2021UDALM: Unsupervised Domain Adaptation through Language Modelingcited by 33
  5. 2023Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Costcited by 33
  6. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
  7. 2023Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detectioncited by 29
  8. 2022M2D2: A Massively Multi-domain Language Modeling Datasetcited by 26
  9. 2021Unsupervised Domain Adaptation: A Reality Checkcited by 24
  10. 2022On the Domain Adaptation and Generalization of Pretrained Language Models: A Surveycited by 22
  11. 2021Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Frameworkcited by 21
  12. 2021Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLPcited by 21
  13. 2021Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarizationcited by 21
  14. 2021Can Explanations Be Useful for Calibrating Black Box Models?cited by 20
  15. 2022A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processingcited by 20
  16. 2023Position: Key Claims in LLM Research Have a Long Tail of Footnotescited by 20
  17. 2021DaN+: Danish Nested Named Entities and Lexical Normalizationcited by 19
  18. 2023Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptationcited by 19
  19. 2022No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrievalcited by 17
  20. 2023Rethinking Semi-supervised Learning with Language Modelscited by 17
  21. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 16
  22. 2022Example-based Hypernetworks for Out-of-Distribution Generalizationcited by 16
  23. 2022Improving the Sample Efficiency of Prompt Tuning with Domain Adaptationcited by 15
  24. 2025AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptationcited by 15
  25. 2021Unsupervised Domain Adaptation with Adaptercited by 14
  26. 2022Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Surveycited by 14
  27. 2023Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Casescited by 14
  28. 2023Maximum Likelihood Estimation is All You Need for Well-Specified Covariate Shiftcited by 14
  29. 2021Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognitioncited by 13
  30. 2022Non-Parametric Domain Adaptation for End-to-End Speech Translationcited by 12
  31. 2022Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptationcited by 12
  32. 2022Discovering Differences in the Representation of People using Contextualized Semantic Axescited by 12
  33. 2022Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Surveycited by 12
  34. 2021DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extractioncited by 11
  35. 2023Measuring the Robustness of NLP Models to Domain Shiftscited by 11
  36. 2021A2Log: Attentive Augmented Log Anomaly Detectioncited by 10
  37. 2023Domain-adaptive Message Passing Graph Neural Networkcited by 10
  38. 2023NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Textscited by 9

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