PFN (Partition Filter Network)
This repository contains codes of the official implementation for the paper A Partition Filter Network for Joint Entity and Relation Extraction EMNLP 2021 [PDF] [PPT]
Quick links
- Model Overview
- Preparation
- Quick Start
- Evaluation on CoNLL04
- Pre-trained Models and Training Logs
- Robustness Against Input Perturbation
- Citation
Model Overview
In this work, we present a new framework equipped with a novel recurrent encoder named partition filter encoder designed for multi-task learning. The encoder enforces bilateral interaction between NER and RE in two ways:
- The shared partition represents inter-task information and is equally accessible to both tasks, allowing for balanced interaction between NER and RE.
- The task partitions represent intra-task information and are formed through concerted efforts of entity and relation gates, making sure that encoding process of entity and relation features are dependent upon each other.
Preparation
Environment Setup
The experiments were performed using one single NVIDIA-RTX3090 GPU. The dependency packages can be installed with the following command:
pip install -r requirements.txt
Also, make sure that the python version is 3.7.10
Data Acquisition and Preprocessing
This is the first work that covers all the mainstream English datasets for evaluation, including [NYT, WEBNLG, ADE, ACE2005, ACE2004, SCIERC, CONLL04]. Please follow the instructions of reademe.md in each dataset folder in ./data/ for data acquisition and preprocessing.
Custom Dataset
If your custom dataset has a large number of triples that contain head-overlap entities (common in Chinese dataset), accuracy of the orignal PFN will not be good.
The orignal one will not be able to decode triples with head-overlap entities. For example, if New York and New York City are both entities, and there exists a RE prediction such as (new, cityof, USA), we cannot know what New corresponds to.
Luckily, the impact on evaluation of English dataset is limited, since such triple is either filtered out (for ADE) or rare (one in test set of SciERC, one in ACE04, zero in other datasets).
You can use our updated PFN-nested to handle the issue. PFN-nested is an enhanced version of PFN. This model is better in leveraging entity tail information and capable of handling nested triple prediction. For usage, replace the files in the root directory with the files in the PFN-nested folder, then follow the directions in Quick Start.
Performance comparison in SciERC
| Model | NER | RE |
|---|---|---|
| PFN | 66.8 | 38.4 |
| PFN-nested | 67.9 | 38.7 |
Quick Start
Model Training
The training command-line is listed below (command for CONLL04 is in Evaluation on CoNLL04):
python main.py \
--data ${NYT/WEBNLG/ADE/ACE2005/ACE2004/SCIERC} \
--do_train \
--do_eval \
--embed_mode ${bert_cased/albert/scibert} \
--batch_size ${20 (for most datasets) /4 (for SCIERC)} \
--lr ${0.00002 (for most datasets) /0.00001 (for SCIERC)} \
--output_file ${the name of your output files, e.g. ace_test} \
--eval_metric ${micro/macro}
After training, you will obtain three files in the ./save/${output_file}/ directory:
- ${output_file}.log records the logging information.
- ${output_file}.txt records loss, NER and RE results of dev set and test set for each epoch.
- ${output_file}.pt is the saved model with best average F1 results of NER and RE in the dev set.
Evaluation on Pre-trained Model
The evaluation command-line is listed as follows:
python eval.py \
--data ${NYT/WEBNLG/ADE/ACE2005/ACE2004/SCIERC} \
--eval_metric ${micro/macro} \
--model_file ${the path of saved model you want to evaluate. e.g. save/ace_test.pt} \
--embed_mode ${bert_cased/albert/scibert}
Inference on Customized Input
If you want to evaluate the model with customized input, please run the following code:
python inference.py \
--model_file ${the path of your saved model} \
--sent ${sentence you want to evaluate, str type restricted}
{model_file} must contain information about the datasets the model trained on (web/nyt/ade/ace/sci) and the type of pretrained embedding the model uses (albert/bert/scibert). For example, model_file could be set as "web_bert.pt"
Example
input:
python inference.py \
--model_file save/sci_test_scibert.pt \
--sent "In this work , we present a new framework equipped with a novel recurrent encoder
named partition filter encoder designed for multi-task learning ."
result:
entity_name: framework, entity type: Generic
entity_name: recurrent encoder, entity type: Method
entity_name: partition filter encoder, entity type: Method
entity_name: multi-task learning, entity type: Task
triple: recurrent encoder, Used-for, framework
triple: recurrent encoder, Part-of, framework
triple: recurrent encoder, Used-for, multi-task learning
triple: partition filter encoder, Hyponym-of, recurrent encoder
triple: partition filter encoder, Used-for, multi-task learning
input:
python inference.py \
--model_file save/ace_test_albert.pt \
--sent "As Williams was struggling to gain production and an audience for his work in the late 1930s ,
he worked at a string of menial jobs that included a stint as caretaker on a chicken ranch in
Laguna Beach , California . In 1939 , with the help of his agent Audrey Wood , Williams was
awarded a $1,000 grant from the Rockefeller Foundation in recognition of his play Battle of
Angels . It was produced in Boston in 1940 and was poorly received ."
result:
entity_name: Williams, entity type: PER
entity_name: audience, entity type: PER
entity_name: his, entity type: PER
entity_name: he, entity type: PER
entity_name: caretaker, entity type: PER
entity_name: ranch, entity type: FAC
entity_name: Laguna Beach, entity type: GPE
entity_name: California, entity type: GPE
entity_name: his, entity type: PER
entity_name: agent, entity type: PER
entity_name: Audrey Wood, entity type: PER
entity_name: Williams, entity type: PER
entity_name: Rockefeller Foundation, entity type: ORG
entity_name: his, entity type: PER
entity_name: Boston, entity type: GPE
triple: caretaker, PHYS, ranch
triple: ranch, PART-WHOLE, Laguna Beach
triple: Laguna Beach, PART-WHOLE, California
Evaluation on CoNLL04
We also run the test on the dataset CoNLL04, but we did not report the results in our paper due to several reasons:
- We are unsure that the baseline results are fairly reported, the problems are discussed in detail in Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
- Hyper-parameter tuning affects the performance considerably in this dataset.
- Page limits
The command for running CoNLL04 is listed below:
python main.py \
--data CONLL04 \
--do_train \
--do_eval \
--embed_mode albert \
--batch_size 10 \
--lr 0.00002 \
--output_file ${the name of your output files} \
--eval_metric micro \
--clip 1.0 \
--epoch 200
Pre-trained Models and Training Logs
We provide you with pre-trained models for NYT/WEBNLG/ACE2005/ACE2004/SCIERC/CONLL04, along with recorded results of each epoch, identical with training results under the specified configurations above.
Download Links
Due to limited space in google drive, 10-fold model files for ADE are not available to you (training record still available).
After downloading the linked files below, unzip them and put ${data}_test.pt in the directory of ./save/ before running eval.py. Also, ${data}_test.txt and ${data}_test.log records the results of each epoch. You should check that out as well.
| Dataset | File Size | Embedding | Download |
|---|---|---|---|
| NYT | 393MB | Bert-base-cased | Link |
| WebNLG | 393MB | Bert-base-cased | Link |
| ACE05 | 815MB | Albert-xxlarge-v1 | Link |
| ACE04 | 3.98GB | Albert-xxlarge-v1 | Link |
| SciERC | 399MB | Scibert-uncased | Link |
| ADE | 214KB | Bert + Albert | Link |
| CoNLL04 | 815MB | Albert-xxlarge-v1 | Link |
Result Display
F1 results on NYT/WebNLG/ACE05/SciERC:
| Dataset | Embedding | NER | RE |
|---|---|---|---|
| NYT | Bert-base-cased | 95.8 | 92.4 |
| WebNLG | Bert-base-cased | 98.0 | 93.6 |
| ACE05 | Albert-xxlarge-v1 | 89.0 | 66.8 |
| SciERC | Scibert-uncased | 66.8 | 38.4 |
F1 results on ACE04:
| 5-fold | 0 | 1 | 2 | 3 | 4 | Average |
|---|---|---|---|---|---|---|
| Albert-NER | 89.7 | 89.9 | 89.5 | 89.7 | 87.6 | 89.3 |
| Albert-RE | 65.5 | 61.4 | 63.4 | 61.5 | 60.7 | 62.5 |
F1 results on CoNLL04:
| Model | Embedding | Micro-NER | Micro-RE |
|---|---|---|---|
| Table-sequence | Albert-xxlarge-v1 | 90.1 | 73.6 |
| PFN | Albert-xxlarge-v1 | 89.6 | 75.0 |
F1 results on ADE:
| 10-fold | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Average |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Bert-NER | 89.6 | 92.3 | 90.3 | 88.9 | 88.8 | 90.2 | 90.1 | 88.5 | 88.0 | 88.9 | 89.6 |
| Bert-RE | 80.5 | 85.8 | 79.9 | 79.4 | 79.3 | 80.5 | 80.0 | 78.1 | 76.2 | 79.8 | 80.0 |
| Albert-NER | 91.4 | 92.9 | 91.9 | 91.5 | 90.7 | 91.6 | 91.9 | 89.9 | 90.6 | 90.7 | 91.3 |
| Albert-RE | 83.9 | 86.8 | 82.8 | 83.2 | 82.2 | 82.4 | 84.5 | 82.3 | 81.9 | 82.2 | 83.2 |
Robustness Against Input Perturbation
We use robustness test to evaluate our model under adverse circumstances. In this case, we use the domain transformation methods of NER from Textflint.
The test files can be found in the folder of ./robustness_data/. Our reported results are evaluated with the linked ACE2005-albert model above. For each test file, move it to ./data/ACE2005/ and rename it as test_triples.json, then run eval.py with the instructions above.
Citation
Please cite our paper if it's helpful to you in your research.
@misc{yan2021partition,
title={A Partition Filter Network for Joint Entity and Relation Extraction},
author={Zhiheng Yan and Chong Zhang and Jinlan Fu and Qi Zhang and Zhongyu Wei},
year={2021},
eprint={2108.12202},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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