KnowPrompt

Code and datasets for our paper "KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction"

Requirements

To install requirements:

pip install -r requirements.txt

Datasets

We provide all the datasets and prompts used in our experiments.

• [SEMEVAL]

• [DialogRE]

• [TACRED-Revisit]

• [Re-TACRED]

• [TACRED]

The expected structure of files is:

knowprompt
 |-- dataset
 |    |-- semeval
 |    |    |-- train.txt       
 |    |    |-- dev.txt
 |    |    |-- test.txt
 |    |    |-- temp.txt
 |    |    |-- rel2id.json
 |    |-- dialogue
 |    |    |-- train.json       
 |    |    |-- dev.json
 |    |    |-- test.json
 |    |    |-- rel2id.json
 |    |-- tacred
 |    |    |-- train.txt       
 |    |    |-- dev.txt
 |    |    |-- test.txt
 |    |    |-- temp.txt
 |    |    |-- rel2id.json
 |    |-- tacrev
 |    |    |-- train.txt       
 |    |    |-- dev.txt
 |    |    |-- test.txt
 |    |    |-- temp.txt
 |    |    |-- rel2id.json
 |    |-- retacred
 |    |    |-- train.txt       
 |    |    |-- dev.txt
 |    |    |-- test.txt
 |    |    |-- temp.txt
 |    |    |-- rel2id.json
 |-- scripts
 |    |-- semeval.sh
 |    |-- dialogue.sh
 |    |-- ...
 

Run the experiments

Initialize the answer words

Use the comand below to get the answer words to use in the training.

python get_label_word.py --model_name_or_path bert-large-uncased  --dataset_name semeval

The {answer_words}.ptwill be saved in the dataset, you need to assign the model_name_or_path and dataset_name in the get_label_word.py.

Split dataset

Download the data first, and put it to dataset folder. Run the comand below, and get the few shot dataset.

python generate_k_shot.py --data_dir ./dataset --k 8 --dataset semeval
cd dataset
cd semeval
cp rel2id.json val.txt test.txt ./k-shot/8-1

You need to modify the k and dataset to assign k-shot and dataset. Here we default seed as 1,2,3,4,5 to split each k-shot, you can revise it in the generate_k_shot.py

Let's run

Our script code can automatically run the experiments in 8-shot, 16-shot, 32-shot and standard supervised settings with both the procedures of train, eval and test. We just choose the random seed to be 1 as an example in our code. Actually you can perform multiple experments with different seeds.

Example for SEMEVAL

Train the KonwPrompt model on SEMEVAL with the following command:

>> bash scripts/semeval.sh  # for roberta-large

As the scripts for TACRED-Revist, Re-TACRED, Wiki80 included in our paper are also provided, you just need to run it like above example.

Example for DialogRE

As the data format of DialogRE is very different from other dataset, Class of processor is also different. Train the KonwPrompt model on DialogRE with the following command:

>> bash scripts/dialogue.sh  # for roberta-base