SpanKL

A Neural Span-Based Continual Named Entity Recognition Model

Source code for AAAI 2023 paper: A Neural Span-Based Continual Named Entity Recognition Model [arxiv]
[Appendix for AAAI paper]

1. Prerequisites

# Environment
- python >= 3.6

# Dependencies
- torch >= 1.8
- transformers == 4.3.0
- rich
- prettytable
- paramiko  # [Option] if need to remotely open file.

2. Dataset

• OntoNotes 5.0
• Few-NERD

We provide the pre-processed datasets in data/ DIR, which keep the same sample-task allocation for split setup experimented in the paper for reproduction.

[Update 20230525🔥] We provide the zip file of data/ as data.zip for convenient, since the original data/ used LFS and need Github quota 😑. Please unzip it before running the code.😊

3. Training

python train_clner.py \
      --gpu 0 \
      --m spankl \
      --corpus onto \
      --setup split \
      --perm perm0
      
# gpu: which gpu to use. (-2: auto allocate gpu -1: use cpu)
# m: types of model (spankl|add|ext)
# corpus: (onto|fewnerd)
# setup: (split|filter)
# perm: task learning order, default perm0 (perm0|perm1|...)

Note that this yields results regarding one of the Task Permutation (e.g., --perm0), while Tab.1 and Tab.2 in the paper are the results averaged over all the Task Permutations.

Update 0305! #1 For convenience, we log the final metric used in the paper at each incremental step (Task) during training, as belows:

...
INFO     Test All Final MacroF1 over Task-Level:{metric}
...
INFO     Test Filter Final MacroF1 over Task-Level:{metric}
...

and also provide an overivew of the performance learned so far:

# Logging example when the running finishs (All tasks have been learned on OntoNotes).
======onto======

onto-0-2022-07-21_10-02-57-1824-spankl_split_perm0

***Test All***
[[87.9   0.    0.    0.    0.    0.   87.9  87.9 ]
 [87.18 93.33  0.    0.    0.    0.   90.43 90.25]
 [88.13 93.41 95.47  0.    0.    0.   92.61 92.34]
 [87.4  93.26 95.16 85.02  0.    0.   90.71 90.21]
 [87.13 92.65 95.05 84.71 81.37  0.   89.38 88.18]
 [87.88 93.02 95.08 85.93 83.04 93.01 90.3  89.66]
 [-0.26 -0.39 -0.39  0.    0.    0.   -0.17  0.  ]]

The table is organized as (Table Format):

                Task1_metric Task2_metric ... MicroF1 MacroF1
Learn to Task1
Learn to Task2
...
BI (backward interference / forgetting)

The last columns are the metrics of each step used in the paper.

More ways to print the above CL metrics:

After and during training, an overview_metric.json file will be generated in model_ckpt/[MODEL_INFO]/ recording the required details, and you can:

• refer to __main__ in print_cl_metric.py to use print_cl_metric() func to remotely print results.
• or refer to the end of train_clner.py to use a wrapped simply_print_cl_metric() func to locally print results.

For any questions please notice the comments in the code or contact me.
Welcome to star or raise issues and PR! :)

4. License

This project is licensed under the Apache License - see the LICENSE file for details.

5. Citation

If you use this work or code, please kindly cite this paper:

@inproceedings{zhang2023spankl,
  title={A Neural Span-Based Continual Named Entity Recognition Model},
  author={Zhang, Yunan and Chen, Qingcai},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2023}
}
or
@article{Zhang_Chen_2023,
 title={A Neural Span-Based Continual Named Entity Recognition Model},
 volume={37},
 url={https://ojs.aaai.org/index.php/AAAI/article/view/26638},
 DOI={10.1609/aaai.v37i11.26638},
 number={11},
 journal={Proceedings of the AAAI Conference on Artificial Intelligence},
 author={Zhang, Yunan and Chen, Qingcai},
 year={2023},
 month={Jun.},
 pages={13993-14001}
 }