Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

Overview

PWC PWC PWC PWC

TDEER 🦌 🦒

Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

Overview

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.

overview

Reproduction Steps

1. Environment

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

2. Prepare Data

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.

3. Download Pretrained BERT

Click 👉 BERT-Base-Cased to download the pretrained model and then decompress to pretrained-bert folder.

4. Train & Eval

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

Pre-trained Models

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

click to show the result screenshot.

2. WebNLG

click to show the result screenshot.

3. NYT11-HRL

click to show the result screenshot.

Citation

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",
}

Acknowledgment

Some of our codes are inspired by weizhepei/CasRel. Thanks for their excellent work.

Contact

If you have any questions about the paper or code, you can

  1. create an issue in this repo;
  2. feel free to contact 1st author at [email protected] / [email protected], I will reply ASAP.
A Light CNN for Deep Face Representation with Noisy Labels

A Light CNN for Deep Face Representation with Noisy Labels Citation If you use our models, please cite the following paper: @article{wulight, title=

Alfred Xiang Wu 715 Nov 05, 2022
Differential Privacy for Heterogeneous Federated Learning : Utility & Privacy tradeoffs

Differential Privacy for Heterogeneous Federated Learning : Utility & Privacy tradeoffs In this work, we propose an algorithm DP-SCAFFOLD(-warm), whic

19 Nov 10, 2022
prior-based-losses-for-medical-image-segmentation

Repository for papers: Benchmark: Effect of Prior-based Losses on Segmentation Performance: A Benchmark Midl: A Surprisingly Effective Perimeter-based

Rosana EL JURDI 9 Sep 07, 2022
Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020).

SentiBERT Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020). https://arxiv.org/abs/20

Da Yin 66 Aug 13, 2022
JupyterNotebook - C/C++, Javascript, HTML, LaTex, Shell scripts in Jupyter Notebook Also run them on remote computer

JupyterNotebook Read, write and execute C, C++, Javascript, Shell scripts, HTML, LaTex in jupyter notebook, And also execute them on remote computer R

1 Jan 09, 2022
Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation"

SharinGAN Official repo for the work titled "SharinGAN: Combining Synthetic and Real Data for Unsupervised GeometryEstimation" The official project we

Koutilya PNVR 23 Oct 19, 2022
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.

CycleGAN PyTorch | project page | paper Torch implementation for learning an image-to-image translation (i.e. pix2pix) without input-output pairs, for

Jun-Yan Zhu 11.5k Dec 30, 2022
这是一个yolo3-tf2的源码,可以用于训练自己的模型。

YOLOV3:You Only Look Once目标检测模型在Tensorflow2当中的实现 目录 性能情况 Performance 所需环境 Environment 文件下载 Download 训练步骤 How2train 预测步骤 How2predict 评估步骤 How2eval 参考资料

Bubbliiiing 68 Dec 21, 2022
The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks

The Implicit Bias of Gradient Descent on Generalized Gated Linear Networks This folder contains the code to reproduce the data in "The Implicit Bias o

Samuel Lippl 0 Feb 05, 2022
This repository contains the implementation of the paper: "Towards Frequency-Based Explanation for Robust CNN"

RobustFreqCNN About This repository contains the implementation of the paper "Towards Frequency-Based Explanation for Robust CNN" arxiv. It primarly d

Sarosij Bose 2 Jan 23, 2022
Real life contra a deep learning project built using mediapipe and openc

real-life-contra Description A python script that translates the body movement into in game control. Welcome to all new real life contra a deep learni

Programminghut 7 Jan 26, 2022
LUKE -- Language Understanding with Knowledge-based Embeddings

LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transf

Studio Ousia 587 Dec 30, 2022
Official Implementation for the paper DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification

DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification Official Implementation for the pape

Anh M. Nguyen 36 Dec 28, 2022
Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Welcome to Yearn Gnosis Safe! Setting up your local environment Infrastructure Deploying Gnosis Safe Prerequisites 1. Create infrastructure for secret

Numan 16 Jul 18, 2022
571 Dec 25, 2022
deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch.

63 Oct 17, 2022
The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Hierarchical Token Semantic Audio Transformer Introduction The Code Repository for "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound

Knut(Ke) Chen 134 Jan 01, 2023
OverFeat is a Convolutional Network-based image classifier and feature extractor.

OverFeat OverFeat is a Convolutional Network-based image classifier and feature extractor. OverFeat was trained on the ImageNet dataset and participat

593 Dec 08, 2022
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

Minqi 297 Dec 12, 2022
Code base for "On-the-Fly Test-time Adaptation for Medical Image Segmentation"

On-the-Fly Adaptation Official Pytorch Code base for On-the-Fly Test-time Adaptation for Medical Image Segmentation Paper Introduction One major probl

Jeya Maria Jose 17 Nov 10, 2022