READ: Improving Relation Extraction from an ADversarial Perspective

Description

This repo contains the source code for the NAACL 2024 findings paper READ: Improving Relation Extraction from an ADversarial Perspective.

Project Structure

├── configs                <- Configuration files
│   ├── experiment               <- Experiment configs
│   ├── mode                     <- Mode configs
│   ├── trainer                  <- Trainer configs
│   ├── config.yaml              <- Main config 
|   └── wandb.yaml               <- Adversarial hyper-parameter config 
│
├── data                   <- Project data
│
├── logs                   <- Logs and saved checkpoints
│
├── preprocess             <- Preprocessing scripts
│
├── saved_models           <- Saved models
│
├── src                    <- Source code
│   ├── stage_1                  <- Stage 1 code: record learning order
│   ├── stage_2                  <- Stage 2 code: contrastive pre-training
│   └── stage_3                  <- Stage 2 code: fine-tuning
│       └── sentence_level       <- Fine-tune for sentence-level relation extraction
│
├── requirements.txt       <- File for installing python dependencies
├── run.py                 <- Controller
├── run_wandb_v2.py        <- Entry for adversarial hyper-parameter searching
└── README.md

Initalize

• Install dependencies from requirements.txt
• Install Apex
• For ERICA models, you can find them here.
• For FineCL models and dataset, you can find them here. Unzip data.zip and then move both data and saved_models into the project's root directory.

Run

• Revise the pretrained_model_path parameter in configs/experiment/stage_3_sentence_re.yaml (FineCL or ERICA).
• Revise the dataset, train_prop, max_epoch and dropout parameters in configs/trainer/sentence_re.yaml. We follow the original settings in FineCL paper.

Hyper-parameter Seaching

• To search the best adversarial hyper-parameters for each dataset-proporation combo, you need to install Wandb.
• Initialize a sweep by running:

wandb sweep --project <propject-name> configs/wandb.yaml

• Then you will get a sweep-ID, start the sweep agent by running:

wandb agent <sweep-ID>

• Or you can directly use the hyper-parameters below:

For ERICA:

		SemEval			ReTACRED			Wiki80
	1%	10%	100%	1%	10%	100%	1%	10%	100%
adv_lr	0.1	0.05	0.1	0.1	0.05	0.1	0.1	0.1	0.02
adv_max_norm	0.6	0.2	0.6	0.6	0.6	0.4	0.6	0.4	0.4
adv_steps	3	3	3	3	3	3	2	3	3

For FineCL

		SemEval			ReTACRED			Wiki80
	1%	10%	100%	1%	10%	100%	1%	10%	100%
adv_lr	0.1	0.1	0.1	0.1	0.1	0.1	0.05	0.05	0.05
adv_max_norm	0.6	0.2	0.2	0.6	0.4	0.6	0.6	0.4	0.6
adv_steps	3	3	3	3	2	3	1	2	3

• Revise the adversarial hyper-parameters in configs/trainer/sentence_re.yaml to the corresponding values and run:

CUDA_VISIBLE_DEVICES=0 python run.py experiment=stage_3_sentence_re.yaml

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Credits: This work began as a fork of the FineCL repository. If you found our code useful, please consider citing: