# Paradigm Shift in Natural Language Processing

**Authors:** Tianxiang Sun, Xiangyang Liu, Xipeng Qiu, Xuanjing Huang  
**arXiv:** [2109.12575](https://arxiv.org/abs/2109.12575)  
**Date:** 2021-09-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2109.12575v2/

## Key points

- Deep learning has led to a shift in natural language processing (NLP) called Paradigm Shift
- Paradigm Shift involves solving one NLP task by reformulating it as another task
- This approach has been successful in improving model performance across various tasks
- Pre-trained language models have played a crucial role in enabling these paradigm shifts
- Researchers leverage large-scale training data and advanced neural network architectures to learn rich representations of language
- Paradigm Shift offers an alternative approach to traditional task-specific modeling methods
- It allows for knowledge transfer between related tasks and improves efficiency
- Unified models capable of handling multiple NLP tasks simultaneously have been developed through Paradigm Shift
- This simplifies model deployment and reduces the need for specialized models for each individual task

## Summary

In the era of deep learning, natural language processing (NLP) tasks have typically been approached using mainstream paradigms. For instance, sequence labeling has been widely adopted to solve tasks like part-of-speech tagging, named entity recognition, and chunking. On the other hand, classification paradigms have been effective for sentiment analysis. However, with the advancements in pre-trained language models, there has been a noticeable shift in NLP called Paradigm Shift. Paradigm Shift refers to the practice of solving one NLP task by reformulating it as another task. This approach has gained significant traction in recent years and has proven to be successful in improving model performance across various tasks. In fact, some of these paradigm shifts have shown great potential in unifying a wide range of NLP tasks, making it possible to build a single model capable of handling diverse tasks. This paper titled "Paradigm Shift in Natural Language Processing" by Tianxiang Sun, Xiangyang Liu, Xipeng Qiu, and Xuanjing Huang explores this phenomenon of paradigm shifts in recent years. The authors review several paradigms that have demonstrated the potential to solve different NLP tasks effectively. The rapid progress of pre-trained language models has played a crucial role in enabling these paradigm shifts. These models leverage large-scale training data and advanced neural network architectures to learn rich representations of language. By leveraging these representations and adapting them to different tasks through transfer learning or fine-tuning techniques, researchers have achieved remarkable results across various NLP domains. The paper highlights how Paradigm Shift offers an alternative approach to traditional task-specific modeling methods. Instead of designing separate models for each specific task, researchers can now reframe a given task into another form that benefits from existing pre-trained models or architectures. This not only improves efficiency but also allows for knowledge transfer between related tasks. Furthermore, the authors emphasize that Paradigm Shift has led to the development of unified models capable of handling multiple NLP tasks simultaneously. This has significant implications for practical applications as it simplifies model deployment and reduces the need for specialized models for each individual task. In conclusion, this paper provides an overview of the paradigm shifts observed in NLP research in recent years. It highlights the potential of these shifts to solve different NLP tasks effectively and unify diverse tasks under a single model.