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How Transferable are Neural Networks in NLP Applications?

arXiv 1603.06111 ↗First posted 19 Mar 2016cs.CL cs.LG cs.NEcited by 23

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Abstract (arXiv)

Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Mar 2016.

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Citing papers per year: 2021: 12; 2022: 3; 2023: 2; 2024: 5; 2025: 1.

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  1. 2023Improving CLIP Training with Language Rewritescited by 216
  2. 2021On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptationcited by 147
  3. 2024A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learningcited by 52
  4. 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35
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  7. 2021Studying Catastrophic Forgetting in Neural Ranking Modelscited by 9
  8. 2022On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learningcited by 7
  9. 2021indicnlp@kgp at DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Languagescited by 4
  10. 2024Glider: Global and Local Instruction-Driven Expert Routercited by 4
  11. 2021Analysis and Prediction of NLP Models Via Task Embeddingscited by 3
  12. 2021Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Unitscited by 2
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  2. 2021Predicting the Success of Domain Adaptation in Text Similaritycited by 1
  3. 2024Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraintscited by 1
  4. 2021Artificial Perceptual Learning: Image Categorization with Weak Supervisioncited by 0
  5. 2021Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarizationcited by 0
  6. 2021Finding Materialized Models for Model Reusecited by 0
  7. 2022Identifying Suitable Tasks for Inductive Transfer Through the Analysis of Feature Attributionscited by 0
  8. 2023Non-parametric, Nearest-neighbor-assisted Fine-tuning for Neural Machine Translationcited by 0
  9. 2024Cross-Demographic Portability of Deep NLP-Based Depression Modelscited by 0
  10. 2024Robust Speech and Natural Language Processing Models for Depression Screeningcited by 0
  11. 2025Rethinking Reasoning in LLMs: Neuro-Symbolic Local RetoMaton Beyond ICL and CoTcited by 0