Source code of the Decouple Processed Continual Relation Extraction (DP-CRE) [DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation].

Continuous Relation Extraction (CRE) aims to incrementally learn relation knowledge from a non-stationary stream of data. One significant challenge in this domain is catastrophic forgetting, where the introduction of new relational tasks can overshadow previously learned information. Unlike traditional replay-based training paradigms that uniformly prioritize all data, we decouple the process of prior information preservation and new knowledge acquisition. In this paper, we introduce the Decouple Processed CRE (DP-CRE) framework. This approach examines alterations in the embedding space as new relation classes emerge, distinctly managing the preservation and acquisition of knowledge. Extensive experiments show that DP-CRE significantly outperforms other CRE baselines across two datasets.

• Python 3.7.16
• PyTorch: 1.13.1(cuda version 11.7).

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

To install PyTorch, you could follow the official guidance of PyTorch.

Then, other dependencies could be installed by running:

pip install -r requirements.txt

Pre-trained BERT weights:

• Download bert-base-uncased into the root directory [google drive].

We use FewRel and TACRED datasets in our experiments.

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

The splited datasets and task orders is conducted in sample.py.

To reproduce the results of main experiment:

python main.py --task_name FewRel
python main.py --task_name TACRED