# A Survey on Transfer Learning in Natural Language Processing

arXiv: https://arxiv.org/abs/2007.04239 · HTML: https://mlsanity.com/paper/2007.04239/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2020.

## Built on by (most cited first, 30 of 33)

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- [Transfer Learning with Clinical Concept Embeddings from Large Language Models](https://mlsanity.com/paper/2409.13893/) (2024)
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- [Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach](https://mlsanity.com/paper/2205.00387/) (2022)
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## Abstract (arXiv)

Deep learning models usually require a huge amount of data. However, these large datasets are not always attainable. This is common in many challenging NLP tasks. Consider Neural Machine Translation, for instance, where curating such large datasets may not be possible specially for low resource languages. Another limitation of deep learning models is the demand for huge computing resources. These obstacles motivate research to question the possibility of knowledge transfer using large trained models. The demand for transfer learning is increasing as many large models are emerging. In this survey, we feature the recent transfer learning advances in the field of NLP. We also provide a taxonomy for categorizing different transfer learning approaches from the literature.