Paradigm Shift in NLP

Welcome to the webpage for "Paradigm Shift in Natural Language Processing". Some resources of the paper are constantly maintained here, such as a full list of papers of paradigm shift, an interactive Sankey diagram to depict the trend of paradigm shift, etc.

What is paradigm shift?

First of all, what is paradigm, and what is paradigm shift?

Paradigm is the general framework to model a class of tasks. For example, sequence labeling (SeqLab) is a popular paradigm to solve named entity recognition (NER). We summarize the mainstream paradigms that are widely used for common NLP tasks as: Class, Matching, SeqLab, MRC, Seq2Seq, Seq2ASeq, (M)LM.

Paradigm shift is a phenomena of solving a task that is usually solved with some paradigm with another paradigm. For example, Li et al. (2020) uses the MRC paradigm to solve NER, which is previously solved with SeqLab, then we can say that the paradigm of NER shifted from SeqLab to MRC.

The figure below shows the observed shift (or transfer) of the seven paradigms in recent years.

Paradigm shift in NLP tasks

We collect the papers of paradigm shift in the table below, which is an extension of the Table 1 in our original paper. This table will be constantly updated.

Trends

To intuitively depict the trend of paradigm shift in NLP, we also draw an interactive Sankey diagram, which is an extension of the Figure 2 in our original paper. Also, this diagram is constantly updated as the table above changed.

Contributing

This line of research is difficult to be comprehensively surveyed, so welcome any additions, modifications, and suggestions! Please feel free to submit pull request or directly contact me.

Citation

If you find this webpage or the paper helpful to your research, please cite our paper:

@article{sun2021paradigmshift,
  title={Paradigm Shift in Natural Language Processing}, 
  author={Tianxiang Sun and Xiangyang Liu and Xipeng Qiu and Xuanjing Huang},
  journal={arXiv preprint arXiv:2109.12575},
  year={2021}
}