Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)
TDEER is an efficient model for joint extraction of entities and relations. Unlike the common decoding approach that predicts the relation between subject and object, we adopt the proposed translating decoding schema: subject + relation -> objects, to decode triples. By the proposed translating decoding schema, TDEER can handle the overlapping triple problem effectively and efficiently. The following figure is an illustration of our models.
We conducted experiments under python3.7 and used GPUs device to accelerate computing.
You should first prepare the tensorflow version in terms of your GPU environment. For tensorflow version, we recommend tensorflow-gpu==1.15.0
.
Then, you can install the other required dependencies by the following script.
pip install -r requirements.txt
We follow weizhepei/CasRel to prepare datas.
For convenience, we have uploaded our processed data in this repository via git-lfs. To use the processed data, you could download the data and decompress it (data.zip
) into the data
folder.
Click 👉BERT-Base-Cased to download the pretrained model and then decompress to pretrained-bert
folder.
You can use run.py
with --do_train
to train the model. After training, you can also use run.py
with --do_test
to evaluate data.
Our training and evaluating commands are as follows:
1. NYT
train:
CUDA_VISIBLE_DEVICES=0 nohup python -u run.py \
--do_train \
--model_name NYT \
--rel_path data/NYT/rel2id.json \
--train_path data/NYT/train_triples.json \
--dev_path data/NYT/test_triples.json \
--bert_dir pretrained-bert/cased_L-12_H-768_A-12 \
--save_path ckpts/nyt.model \
--learning_rate 0.00005 \
--neg_samples 2 \
--epoch 200 \
--verbose 2 > nyt.log &
evaluate:
CUDA_VISIBLE_DEVICES=0 python run.py \
--do_test \
--model_name NYT \
--rel_path data/NYT/rel2id.json \
--test_path data/NYT/test_triples.json \
--bert_dir pretrained-bert/cased_L-12_H-768_A-12 \
--ckpt_path ckpts/nyt.model \
--max_len 512 \
--verbose 1
You can evaluate other data by specifying --test_path
.
2. WebNLG
train:
CUDA_VISIBLE_DEVICES=0 nohup python -u run.py \
--do_train \
--model_name WebNLG \
--rel_path data/WebNLG/rel2id.json \
--train_path data/WebNLG/train_triples.json \
--dev_path data/WebNLG/test_triples.json \
--bert_dir pretrained-bert/cased_L-12_H-768_A-12 \
--save_path ckpts/webnlg.model \
--max_sample_triples 5 \
--neg_samples 5 \
--learning_rate 0.00005 \
--epoch 300 \
--verbose 2 > webnlg.log &
evaluate:
CUDA_VISIBLE_DEVICES=0 python run.py \
--do_test \
--model_name WebNLG \
--rel_path data/WebNLG/rel2id.json \
--test_path data/WebNLG/test_triples.json \
--bert_dir pretrained-bert/cased_L-12_H-768_A-12 \
--ckpt_path ckpts/webnlg.model \
--max_len 512 \
--verbose 1
You can evaluate other data by specifying --test_path
.
3. NYT11-HRL
train:
CUDA_VISIBLE_DEVICES=0 nohup python -u run.py \
--do_train \
--model_name NYT11-HRL \
--rel_path data/NYT11-HRL/rel2id.json \
--train_path data/NYT11-HRL/train_triples.json \
--dev_path data/NYT11-HRL/test_triples.json \
--bert_dir pretrained-bert/cased_L-12_H-768_A-12 \
--save_path ckpts/nyt11hrl.model \
--learning_rate 0.00005 \
--neg_samples 1 \
--epoch 100 \
--verbose 2 > nyt11hrl.log &
evaluate:
CUDA_VISIBLE_DEVICES=0 python run.py \
--do_test \
--model_name NYT11-HRL \
--rel_path data/NYT/rel2id.json \
--test_path data/NYT11-HRL/test_triples.json \
--bert_dir pretrained-bert/cased_L-12_H-768_A-12 \
--ckpt_path ckpts/nyt11hrl.model \
--max_len 512 \
--verbose 1
We released our pre-trained models for NYT, WebNLG, and NYT11-HRL datasets, and uploaded them to this repository via git-lfs.
You can download pre-trained models and then decompress them (ckpts.zip
) to the ckpts
folder.
To use the pre-trained models, you need to download our processed datasets and specify --rel_path
to our processed rel2id.json
.
To evaluate by the pre-trained models, you can use above commands and specify --ckpt_path
to specific model.
In our setting, NYT, WebNLG, and NYT11-HRL achieve the best result on Epoch 86, 174, and 23 respectively.
1. NYT
2. WebNLG
3. NYT11-HRL
If you use our code in your research, please cite our work:
@inproceedings{li-etal-2021-tdeer,
title = "{TDEER}: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations",
author = "Li, Xianming and
Luo, Xiaotian and
Dong, Chenghao and
Yang, Daichuan and
Luan, Beidi and
He, Zhen",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.635",
pages = "8055--8064",
}
Some of our codes are inspired by weizhepei/CasRel. Thanks for their excellent work.
If you have any questions about the paper or code, you can
- create an issue in this repo;
- feel free to contact 1st author at niming.lxm@alipay.com / xmlee97@gmail.com, I will reply ASAP.