This project provides a solution for training and validating seq2seq models for information extraction. The method can be applied for any text-only type of document, such as legal, registration, news, etc. The project extracts information by question answering.
In this work, we evaluate sequence-to-sequence models as an alternative to token-level classification methods to extract information from documents. T5 models are finetuned to jointly extract the information and generate the output in a structured format. Post-processing steps are learned during training, eliminating the need for rule-based methods and simplifying the pipeline.
Neither the models weights nor the datasets are available for ethical issues. But we make efforts to release the source code that works for different models of T5 family, and can be easily extended for new datasets and different languages.
Clone the repository and install via:
pip install .Configure the parameters in params.yaml. Then, preprocess the datasets by running:
python information_extraction_t5/data/convert_dataset_to_squad.py -c params.yamlStart the fine-tuning experiment via:
python information_extraction_t5/train.py -c params.yamlThe code executes both the training and inference using the checkpoint with best exact matching over the validation set, as well as the complete post-processing that involves especially:
- selecting the most likely answer (the sliding window that provided the highest-probability response), and
- breaking compound answers in clean and individual sub-answers.
It also computes metrics and generates the following output files:
metrics_by_typenames.json: JSON file with exact matching and F1-score for each field, each dataset and all the documents.metrics_by_documents.json: JSON file with exact matching and F1-score for each document, each dataset and all the documents.outputs_by_typenames.txt: TXT file with labels, predictions, document id, probability of selected window, and window id, grouped by fields.outputs_by_documents.txt: TXT file with labels, predictions, field id, probability of selected window, and window id, grouped by documents.output_sheet.xlsl: Excel file with field id, labels, predictions and probs grouped by documents.output_sheet_client.xlsl: Excel file with labels, predictions, probs and metrics, organized as one sheet for each dataset.
Note that, for now, only a tiny synthetic dataset is available. To extend for new datasets, please consult this section.
Assuming you have finished the training and inference and want to use a different testing dataset, an intermediary checkpoint or even explore a different post-processing function or parameter, just run:
python information_extraction_t5/predict.py -c params.yamlFor cases that require a new inference round, remember to set use_cache_predictions: false to overwrite the cache. Otherwise, if you intend to rerun the post-processing, set use_cache_predictions: true.
To know all the settings related to pre-processing, training, inference and pos-processing stages, please run:
python information_extraction_t5/train.py --helpYou will find an extensive list of parameters because it is inheriting Pytorch-Lightning's Trainer arguments.
Give special attention only to the parameters that are in the params.yaml file.
In this section we explain how to include new datasets for running fine-tuning and inference. It is important to emphasize that the four datasets that have been originally applied in the project cannot be released for ethical issues.
There are two preliminar steps when extending the project for new datasets.
The original field names of the datasets can be noisy, not natural. One important step is converting those irregular names into natural ones. The natural names will be used as clues in the answers.
For each dataset, it is necessary to map field names (we call it as type-name in the code) to types and vice-versa. The types are used as clues in brackets in T5 outputs. The field names are recovered in post-processing stage.
Each dataset has it own TYPENAME_TO_TYPE dictionary. We strongly recommend that the types used in all the projects be consistent, and as generic as possible. For example, using CPF/CNPJ for all CPFs and CNPJs, regardless of being a consultant, current account holder, business partner, land owner, etc.
If your dataset does not follow the required SQuAD format and you intend to use the pre-processing code, before starting the conversion it is necessary to formulate the questions.
Each dataset will have a particular dictionary of questions, in which the key is the field name (we call it as type-name in the code) and the value is a list of questions.
HINT: We use one list of questions for each field as a strategy to augment the dataset. You can use the data augmentation by setting train_choose_question: all in params.yaml. Use random to select one question randomly, or first to get the first one for each field.
If you have a compound information (the value is an internal dictionary), we recommend representing the dict as an OrderedDict in order to use the dictionary keys as field signature, ensure a possíble compound answer will have it sub-answers in an inmutable order.
As the project aims at extracting information using QA modality, we adopt the SQuAD as the format of the datasets, with a few adaptations. Below we present an example to illustrate the structure of the dataset file and describe the adaptations to enable the use of sliding windows and the reference of each pair [document, field], in order to enable an effective metric computation for each document, dataset and field.
{
"data": [
{
"title": "318",
"paragraphs": [
{
"context": "Proposta de Abertura de Conta, Contrata\u00e7\u00e3o de Cr\u00e9dito e\nAdes\u00e3o a Produtos e Servi\u00e7os Banc\u00e1rios - Pessoa F\u00edsica\nID00147\nAg\u00eancia N\u00ba\n1234\nConta Corrente 0011-2347-0000809875312\nCondi\u00e7\u00e3o de Movimenta\u00e7\u00e3o da Conta X Individual\nAltera\u00e7\u00e3o cadastral\nAngariador (matr\u00edcula) L\n00098961\nDados B\u00e1sicos do Titular\nCPF\n516.759.760-90\n...",
"qas": [
{
"answers": [
{
"answer_start": 157,
"text": "[Ag\u00eancia]: 2347"
}
],
"question": "Qual \u00e9 o n\u00famero da ag\u00eancia?",
"id": "form.agencia"
},
{
"answers": [
{
"answer_start": -1,
"text": "[Nome]: N/A"
}
],
"question": "Qual \u00e9 o nome?",
"id": "form.nome_completo"
}
]
},
{
"context": "...\nNome Completo ANA MADALENA SILVEIRA ALVES\nDocumento de Identifica\u00e7\u00e3o CNH CTPS Entidade de Classe Mercosul Passaporte\nProtocolo Refugiado\nRIC RNE\nCIE Guia de Acolhimento ao Menor Registro Nacional Migrat\u00f3rio\nN\u00b0 Documento / N\u00b0 da S\u00e9rie (CTPS)\n73258674 \u00d3rg\u00e3o Emissor SSP\nUF BA\nData de Emiss\u00e3o 21/07/2018 Data de Vcto (passaporte/CNH).",
"qas": [
{
"answers": [
{
"answer_start": -1,
"text": "[Ag\u00eancia]: N/A"
}
],
"question": "Qual \u00e9 o n\u00famero da ag\u00eancia?",
"id": "form.agencia"
},
{
"answers": [
{
"answer_start": 18,
"text": "[Nome]: ANA MADALENA SILVEIRA ALVES"
}
],
"question": "Qual \u00e9 o nome?",
"id": "form.nome_completo"
}
]
}
]
}
],
"version": "0.1"
}The example we presented herein includes one document whose id = 318, and context fits into two sliding windows. We adapted SQuAD format by transforming the list of different documents related to the same theme into a list of different sliding windows of the same document. For each document, reference in title, paragraphs is a list of dictionaries that have context and an internal dictionary of QAs.
The dictionaries of QAs follows the same intuition of SQuAD dataset, but we included in id the signature of QA, that involves the project (dataset name) and the field. This is very important since enables to get metrics not only for the datasets altogether, but also for each dataset individually as well as for each field.
Assuming you have a dataset already pre-processed, in SQuAD format, to include it in the project you may choose a name for it and edit the following parameters in the params.yaml file:
project: [
form,
]
train_file: data/processed/train-v0.1.json
valid_file: data/processed/dev-v0.1.json
test_file: data/processed/test-v0.1.jsonNote that it's possible to include several datasets in the list of projects, but each {train, valid, test}_file includes the examples of all the datasets listed in project.
If your dataset is not in the complex SQuAD-like format with the document divided into sliding windows, the pairs of question-answers, the correct qa-id, don't worry! We are releasing a code to convert the dataset to the expected format.
What you need to do is just ensure the dataset follows the format of a basic JSON: a dictionary of documents, in which each key is the document-id, and each value is an internal dict that must have the key "text" with the respective document content as value, and other pairs key-values representing the fields the document has.
You can visualize here one raw dataset that is ready to be converted into SQuAD format. NOTE: If you want to extract compound information (using compound QA feature) for the one compound field, such as address, the value of the respective key must be another dictionary with the expected information.
Thus, to generate a SQuAD-like dataset illustrated in the previous subsection, just set the parameters as below:
project: [
form,
]
raw_data_file: [
data/raw/sample_train.json,
]
raw_valid_data_file: [
null,
]
raw_test_data_file: [
data/raw/sample_test.json,
]
train_file: data/processed/train-v0.1.json
valid_file: data/processed/dev-v0.1.json
test_file: data/processed/test-v0.1.json
type_names: [
form.agencia,
form.nome_completo,
]The parameter names are intuitive. You can include any number of dataset names and their respected train, validation and test paths (the four lists must have the same number of parameters). If any of the datasets does not have a validation subset, just include null in the position, and a fraction of valid_percent of the training set will be moved for validation set.
Finally, just run the command below to get the listed datasets converted to SQuAD format and saved as {train, valid, test}_file.
python information_extraction_t5/data/convert_dataset_to_squad.py -c params.yamlThe released code for dataset pre-processing does not include the features sentence-ids and raw-text formats as it would require more complex and ellaborated raw dataset, whose structure must include annotations of positions and texts both in raw and canonical formats. Those features are important only for industrial applications, but, depending of the dataset size, you can manually include answer_start for each qa, and setting the answer as N/A if it does not fit in the window. For training the model to extract canonical and raw-text information, you can change both the questions and answers as:
Q: What is the state?
A: [State]: São Paulo
Q: What is the state and how does it appear in the text?
A: [State]: SP [appears in the text]: São Paulo
@inproceedings{pires2022seq2seq,
title = {Sequence-to-Sequence Models for Extracting Information from Registration and Legal Documents},
author = {Pires, Ramon and de Souza, Fábio C. and Rosa, Guilherme and Lotufo, Roberto A. and Nogueira, Rodrigo},
publisher = {arXiv},
doi = {10.48550/ARXIV.2201.05658},
url = {https://arxiv.org/abs/2201.05658},
year = {2022},
}