hemingkx / CDec

Codes for our paper "Enhancing Continual Relation Extraction via Classifier Decomposition" (Findings of ACL2023)

Classifier Decomposition

Codes for our paper "Enhancing Continual Relation Extraction via Classifier Decomposition (Findings of ACL2023). In this paper:

• We find that CRE models suffer from classifier and representation biases when learning the new relations.
• We propose a simple yet effective classifier decomposition framework with empirical initialization and adversarial tuning to alleviate these two biases.
• Extensive experiments on FewRel and TACRED verify the effectiveness of our method.

Environment

• Python >= 3.7
• Torch >= 1.5.0

conda create -n cdec python=3.8
conda activate cdec
pip install -r requirements.txt

Dataset

We use two datasets in our experiments, FewRel and TACRED:

• FewRel: data/data_with_marker.json
• TACRED: data/data_with_marker_tacred.json

We construct 5 CRE task sequences in both FewRel and TACRED the same as RP-CRE and CRL.

Run

bash ./bash/[dataset]/cdec.sh
    - dataset: the dataset name, e.g.,:
        - fewrel/tacred

Note

This code is based on ACA (https://github.com/Wangpeiyi9979/ACA).

Citation

@inproceedings{Xia:2023CDec,
  author       = {Heming Xia and
                  Peiyi Wang and
                  Tianyu Liu and
                  Binghuai Lin and
                  Yunbo Cao and
                  Zhifang Sui},
  editor       = {Anna Rogers and
                  Jordan L. Boyd{-}Graber and
                  Naoaki Okazaki},
  title        = {Enhancing Continual Relation Extraction via Classifier Decomposition},
  booktitle    = {Findings of the Association for Computational Linguistics: {ACL} 2023,
                  Toronto, Canada, July 9-14, 2023},
  pages        = {10053--10062},
  publisher    = {Association for Computational Linguistics},
  year         = {2023},
  url          = {https://doi.org/10.18653/v1/2023.findings-acl.638},
  doi          = {10.18653/V1/2023.FINDINGS-ACL.638},
  timestamp    = {Thu, 10 Aug 2023 12:35:45 +0200},
  biburl       = {https://dblp.org/rec/conf/acl/XiaW0LCS23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}