• Code for Unified Structure Generation for Universal Information Extraction
• Please contact Yaojie Lu (@luyaojie) for questions and suggestions.

• [2022-06-12] Update pre-training code.
• [2022-05-10] Update data preprocessing code.

General

• Python (verified on 3.8)
• CUDA (verified on 11.1/10.2)

Python Packages CUDA 10.2

conda create -n uie python=3.8
conda install -y pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt

CUDA 11.1

conda create -n uie python=3.8
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

Details of preprocessing see Data preprocessing.

After that, please link the preprocessed dataset as:

ln -s dataset_processing/converted_data/ data

Data folder contains seven files：

data/text2spotasoc/absa/14lap
├── entity.schema       # Entity Types for converting SEL to Record
├── relation.schema     # Relation Types for converting SEL to Record
├── event.schema        # Event Types for converting SEL to Record
├── record.schema       # Spot/Asoc Type for constructing SSI
├── test.json
├── train.json
└── val.json

train/val/test.json are data files, and each line is a JSON instance. Each JSON instance contains text and record fields, in which text is plain text, and record is the SEL representation of the extraction structure. Details definition see DATASETS.md.

Note:

• Use the extra character of T5 as the structure indicators, such as <extra_id_0>, <extra_id_1>, <extra_id_5>.

• record.schema is the record schema file for building SSI. It contains three lines: the first line is spot name list, the second line is asoc name list. And the third line is spot-to-asoc dictionary (do not use in code, can be ignored).

  ["aspect", "opinion"]
  ["neutral", "positive", "negative"]
  {"aspect": ["neutral", "positive", "negative"], "opinion": []}
  

You can find the pre-trained models as following CAS Cloud Box/Google Drive links or download models using command gdown (pip install gdown).

uie-en-base [CAS Cloud Box] [Google Drive] [Huggingface]

uie-en-large [CAS Cloud Box] [Google Drive] [Huggingface]

uie-char-small (chinese) [CAS Cloud Box]

# Example of Google Drive
gdown 12Dkh6KLDPvXrkQ1I-1xLqODQSYjkwnvs && unzip uie-base-en.zip
gdown 15OFkWw8kJA1k2g_zehZ0pxcjTABY2iF1 && unzip uie-large-en.zip

Put all models to hf_models/ for default running scripts.

First make directories otuput.

Training scripts as follows:

• run_uie_finetune.py: Python code entry
• run_uie_finetune.bash: Model training and evaluating process script.
• scripts_exp/run_exp.bash: Model environment configuration and parameter setting entry.

The command for the training is as follows (see bash scripts and Python files for the corresponding command-line arguments):

. config/data_conf/base_model_conf_absa.ini  && model_name=uie-base-en dataset_name=absa/14lap bash scripts_exp/run_exp.bash

• config/data_conf/base_model_conf_absa.ini refers to using the training settings in base_model_conf_absa.ini
• model_name=uie-base-en refers to using uie-base-en.
• dataset_name=absa/14lap refers to the dataset path.

Trained models are saved in the output_dir specified by run_uie_finetune.bash.

Simple Training Command

bash run_uie_finetune.bash -v -d 0 \
  -b 16 \
  -k 3 \
  --lr 1e-4 \
  --warmup_ratio 0.06 \
  -i absa/14lap \
  --epoch 50 \
  --spot_noise 0.1 \
  --asoc_noise 0.1 \
  -f spotasoc \
  --epoch 50 \
  --map_config config/offset_map/closest_offset_en.yaml \
  -m hf_models/uie-base-en \
  --random_prompt

Progress logs

...
***** Running training *****
  Num examples = 906
  Num Epochs = 50
  Instantaneous batch size per device = 16
  Total train batch size (w. parallel, distributed & accumulation) = 16
  Gradient Accumulation steps = 1
  Total optimization steps = 2850
  Num examples = 219
  Batch size = 64
...

Final Result (specific scores may different from different machines and environments)

...
test offset-rel-strict-P 67.01461377870564
test offset-rel-strict-R 59.11602209944752
test offset-rel-strict-F1 62.81800391389433
...

[TODO] Add detailed decription.

We construct different sequence-to-sequence tasks using different data collators.

• For pre-training, HybirdDataCollator constructs different seq2seq pairs for different tasks, and DataCollatorForMetaSeq2Seq constructs ssi with Sampling Strategy.
• For fine-tuning, DataCollatorForMetaSeq2Seq constructs the dynamic seq2seq pair with Rejection Mechanism.

We unify different types of (text, strcuture) pairs for pre-training with HybirdDataCollator. It contains multiple data collators for different instances:

• DataCollatorForMetaSeq2Seq for pair task, similiar to fine-tune stage
• DataCollatorForSeq2Seq for record task
• DataCollatorForT5MLM for text task

Sampling Strategy and Rejection Mechanism can be adopted in the training process.

• uie/seq2seq/data_collator/meta_data_collator.py class DataCollatorForMetaSeq2Seq is for collating data, class DynamicSSIGenerator is for prompt sampling
• run_uie_finetune.py class DataTrainingArguments contains related parameters

Related parameters in class DataTrainingArguments are briefly introduced here:

• About Sampling Strategy

    - max_prefix_length       Maximum length of SSI
    - ordered_prompt          Whether to sort the spot/asoc of SSI or not
    - record_schema           record schema read from record.schema

• About Rejection Mechanism

  - spot_noise              The noise rate of null spot
  - asoc_noise              The noise rate of null asoc

To verify the performance of the UIE requires converting the generated SEL expression into Record and then evaluating it.

After training, pred_folder will contain 'eval_preds_seq2seq.txt' or 'test_preds_seq2seq.txt'

 $ python scripts/sel2record.py -h     
usage: sel2record.py [-h] [-g GOLD_FOLDER] [-p PRED_FOLDER [PRED_FOLDER ...]] [-c MAP_CONFIG] [-d DECODING] [-v]

optional arguments:
  -h, --help            show this help message and exit
  -g GOLD_FOLDER        folder of golden answer
  -p PRED_FOLDER [PRED_FOLDER ...]
                        multiple different prediction folders
  -c MAP_CONFIG, --config MAP_CONFIG
                        offset matching strategy configuration file, more configuration files are placed in config/offset_map
  -d DECODING           specify structure parser, default is SpotAsoc structure
  -v, --verbose         print more detailed log information

After converting, pred_folder will contain 'eval_preds_record.txt' or 'test_preds_record.txt'

 $ python scripts/eval_extraction.py -h   
usage: eval_extraction.py [-h] [-g GOLD_FOLDER] [-p PRED_FOLDER [PRED_FOLDER ...]] [-v] [-w] [-m] [-case]

optional arguments:
  -h, --help            show this help message and exit
  -g GOLD_FOLDER        Golden Dataset folder
  -p PRED_FOLDER [PRED_FOLDER ...]
                        Predicted model folder
  -v                    Show more information during running
  -w                    Write evaluation results to predicted folder
  -m                    Refers to the matching policy
  -case                 Show case study

To verify the effect of structure parser, we took the golden answer SEL as the prediction result, and evaluate its performance.

bash scripts/check_offset_map_gold_as_pred.bash <data-folder> <map-config>

If this repository helps you, please cite this paper:

Yaojie Lu, Qing Liu, Dai Dai, Xinyan Xiao, Hongyu Lin, Xianpei Han, Le Sun, Hua Wu. Unified Structure Generation for Universal Information Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5755–5772, Dublin, Ireland. Association for Computational Linguistics.

@inproceedings{lu-etal-2022-unified,
    title = "Unified Structure Generation for Universal Information Extraction",
    author = "Lu, Yaojie  and
      Liu, Qing  and
      Dai, Dai  and
      Xiao, Xinyan  and
      Lin, Hongyu  and
      Han, Xianpei  and
      Sun, Le  and
      Wu, Hua",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.395",
    pages = "5755--5772",
}

The code is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License for Noncommercial use only. Any commercial use should get formal permission first.