RelationPrompt

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RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction

This repository implements our ACL Findings 2022 research paper RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction. The goal of Zero-Shot Relation Triplet Extraction (ZeroRTE) is to extract relation triplets of the format (head entity, tail entity, relation), despite not having annotated data for the test relation labels.

Installation

• Python 3.7
• If your GPU uses CUDA 11, first install the specific PyTorch: pip install torch==1.10.0 --extra-index-url https://download.pytorch.org/whl/cu113
• Install requirements: pip install -r requirements.txt or conda env create --file environment.yml
• Download and extract the datasets here to outputs/data/splits/zero_rte
• FewRel Pretrained Model (unseen=10, seed=0)
• Wiki-ZSL Pretrained Model (unseen=10, seed=0)

Data Exploration |

from wrapper import Dataset

data = Dataset.load(path)
for s in data.sents:
    print(s.tokens)
    for t in s.triplets:
        print(t.head, t.tail, t.label)

Generate with Pretrained Model |

from wrapper import Generator

model = Generator(load_dir="gpt2", save_dir="outputs/wrapper/fewrel/unseen_10_seed_0/generator")
model.generate(labels=["location", "religion"], path_out="synthetic.jsonl")

Extract with Pretrained Model |

from wrapper import Extractor

model = Extractor(load_dir="facebook/bart-base", save_dir="outputs/wrapper/fewrel/unseen_10_seed_0/extractor_final")
model.predict(path_in=path_test, path_out="pred.jsonl")

Model Training |

Train the Generator and Extractor models:

from pathlib import Path
from wrapper import Generator, Extractor

generator = Generator(
    load_dir="gpt2",
    save_dir=str(Path(save_dir) / "generator"),
)
extractor = Extractor(
    load_dir="facebook/bart-base",
    save_dir=str(Path(save_dir) / "extractor"),
)
generator.fit(path_train, path_dev)
extractor.fit(path_train, path_dev)

Generate synthetic data with relation triplets for test labels:

generator.generate(labels_test, path_out=path_synthetic)

Train the final Extractor model using the synthetic data and predict on test sentences:

extractor_final = Extractor(
    load_dir=str(Path(save_dir) / "extractor" / "model"),
    save_dir=str(Path(save_dir) / "extractor_final"),
)
extractor_final.fit(path_synthetic, path_dev)
extractor_final.predict(path_in=path_test, path_out=path_pred)

Experiment Scripts

Run training in wrapper.py (You can change "fewrel" to "wiki" or unseen to 5/10/15 or seed to 0/1/2/3/4):

python wrapper.py main \
--path_train outputs/data/splits/zero_rte/fewrel/unseen_10_seed_0/train.jsonl \                                       
--path_dev outputs/data/splits/zero_rte/fewrel/unseen_10_seed_0/dev.jsonl \                                           
--path_test outputs/data/splits/zero_rte/fewrel/unseen_10_seed_0/test.jsonl \                                         
--save_dir outputs/wrapper/fewrel/unseen_10_seed_0   

Run evaluation (Single-triplet setting)

python wrapper.py run_eval \                                                                                               
--path_model outputs/wrapper/fewrel/unseen_10_seed_0/extractor_final \                                                  
--path_test outputs/data/splits/zero_rte/fewrel/unseen_10_seed_0/test.jsonl \
--mode single

Run evaluation (Multi-triplet setting)

python wrapper.py run_eval \                                                                                               
--path_model outputs/wrapper/fewrel/unseen_10_seed_0/extractor_final \                                                  
--path_test outputs/data/splits/zero_rte/fewrel/unseen_10_seed_0/test.jsonl \
--mode multi

Research Citation

If the code is useful for your research project, we appreciate if you cite the following paper:

@inproceedings{chia-etal-2022-relationprompt,
    title = "RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction",
    author = "Chia, Yew Ken and Bing, Lidong and Poria, Soujanya and Si, Luo",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    year = "2022",
    url = "https://arxiv.org/abs/2203.09101",
    doi = "https://doi.org/10.48550/arXiv.2203.09101",
}