Welcome to OmniEvent’s documentation!

Overview

OmniEvent is a powerful open-source toolkit for event extraction, including event detection and event argument extraction. We comprehensively cover various paradigms and provide fair and unified evaluations on widely-used English and Chinese datasets. Modular implementations make OmniEvent highly extensible.

Highlights

• Comprehensive Capability

  • Support to do Event Extraction at once, and also to independently do its two subtasks: Event Detection, Event Argument Extraction.

  • Cover various paradigms: Token Classification, Sequence Labeling, MRC (QA) and Seq2Seq, are deployed.

  • Implement Transformers-based (BERT, T5, etc.) and classical models (CNN, LSTM, CRF, etc.) are implemented.

  • Both Chinese and English are supported for all event extraction sub-tasks, paradigms and models.

• Modular Implementation

  • All models are decomposed into four modules:

    • Input Engineering: Prepare inputs and support various input engineering methods like prompting.

    • Backbone: Encode text into hidden states.

    • Aggregation: Fuse hidden states (e.g., select [CLS], pooling, GCN) to the final event representation.

    • Output Head: Map the event representation to the final outputs, such as Linear, CRF, MRC head, etc.

• Unified Benchmark & Evaluation

  • Various datasets are processed into a unified format.

  • Predictions of different paradigms are all converted into a unified candidate set for fair evaluations.

  • Four evaluation modes (gold, loose, default, strict) well cover different previous evaluation settings.

• Big Model Training & Inference

  • Efficient training and inference of big models for event extraction are supported with BMTrain.

• Easy to Use & Highly Extensible

  • Datasets can be downloaded and processed with a single command.

  • Fully compatible with 🤗 Transformers and its Trainer).

  • Users can easily reproduce existing models and build customized models with OmniEvent.

Installation

With pip

This repository is tested on Python 3.9+, Pytorch 1.12.1+. OmniEvent can be installed with pip as follows:

pip install OmniEvent

Easy Start

OmniEvent provides ready-to-use models for the users. Examples are shown below.

Make sure you have installed OmniEvent as instructed above. Note that it may take a few minutes to download checkpoint for the first time.

Train your Own Model with OmniEvent

OmniEvent can help users easily train and evaluate their customized models on a specific dataset.

We show a step-by-step example of using OmniEvent to train and evaluate an Event Detection model on ACE-EN dataset in the Seq2Seq paradigm. More examples are shown in examples.

Step 1: Process the dataset into the unified format

We provide standard data processing scripts for commonly-adopted datasets. Checkout the details in scripts/data_processing.

dataset=ace2005-en  # the dataset name
cd scripts/data_processing/$dataset
bash run.sh

Step 2: Set up the customized configurations

We keep track of the configurations of dataset, model and training parameters via a single *.yaml file. See /configs for details.

>>> from OmniEvent.arguments import DataArguments, ModelArguments, TrainingArguments, ArgumentParser
>>> from OmniEvent.input_engineering.seq2seq_processor import type_start, type_end

>>> parser = ArgumentParser((ModelArguments, DataArguments, TrainingArguments))
>>> model_args, data_args, training_args = parser.parse_yaml_file(yaml_file="config/all-datasets/ed/s2s/ace-en.yaml")

>>> training_args.output_dir = 'output/ACE2005-EN/ED/seq2seq/t5-base/'
>>> data_args.markers = ["<event>", "</event>", type_start, type_end]

Step 3: Initialize the model and tokenizer

OmniEvent supports various backbones. The users can specify the model and tokenizer in the config file and initialize them as follows.

>>> from OmniEvent.backbone.backbone import get_backbone
>>> from OmniEvent.model.model import get_model

>>> backbone, tokenizer, config = get_backbone(model_type=model_args.model_type,
                                               model_name_or_path=model_args.model_name_or_path,
                                               tokenizer_name=model_args.model_name_or_path,
                                               markers=data_args.markers,
                                               new_tokens=data_args.markers)
>>> model = get_model(model_args, backbone)

Step 4: Initialize dataset and evaluation metric

OmniEvent prepares the DataProcessor and the corresponding evaluation metrics for different task and paradigms.

Note

Note that the metrics here are paradigm-dependent and are not used for the final unified evaluation.

>>> from OmniEvent.input_engineering.seq2seq_processor import EDSeq2SeqProcessor
>>> from OmniEvent.evaluation.metric import compute_seq_F1

>>> train_dataset = EDSeq2SeqProcessor(data_args, tokenizer, data_args.train_file)
>>> eval_dataset = EDSeq2SeqProcessor(data_args, tokenizer, data_args.validation_file)
>>> metric_fn = compute_seq_F1

Step 5: Define Trainer and train

OmniEvent adopts Trainer from 🤗 Transformers) for training and evaluation.

>>> from OmniEvent.trainer_seq2seq import Seq2SeqTrainer

>>> trainer = Seq2SeqTrainer(
        args=training_args,
        model=model,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=metric_fn,
        data_collator=train_dataset.collate_fn,
        tokenizer=tokenizer,
    )
>>> trainer.train()

Step 6: Unified Evaluation

Since the metrics in Step 4 depend on the paradigm, it is not fair to directly compare the performance of different paradigms.

OmniEvent evaluates models of different paradigms in a unifed manner, where the predictions of different models are converted to word-level and then evaluated.

>>> from OmniEvent.evaluation.utils import predict, get_pred_s2s
>>> from OmniEvent.evaluation.convert_format import get_trigger_detection_s2s

>>> logits, labels, metrics, test_dataset = predict(trainer=trainer, tokenizer=tokenizer, data_class=data_class,
                                                    data_args=data_args, data_file=data_args.test_file,
                                                    training_args=training_args)
>>> # paradigm-dependent metrics
>>> print("{} test performance before converting: {}".formate(test_dataset.dataset_name, metrics["test_micro_f1"]))
ACE2005-EN test performance before converting: 66.4215686224377

>>> preds = get_pred_s2s(logits, tokenizer)
>>> # convert to the unified prediction and evaluate
>>> pred_labels = get_trigger_detection_s2s(preds, labels, data_args.test_file, data_args, None)
ACE2005-EN test performance after converting: 67.41016109045849

For those datasets whose test set annotations are not given, such as MAVEN and LEVEN, OmniEvent provide APIs to generate submission files. See dump_result.py) for details.

Supported Datasets & Models & Contests

Continually updated. Welcome to add more!

Datasets

Language	Domain	Task	Dataset
English	General	ED	MAVEN
English	General	ED EAE	ACE-EN
English	General	ED EAE	ACE-DYGIE
English	General	ED EAE	RichERE (KBP + ERE)
Chinese	Legal	ED	LEVEN
Chinese	General	ED EAE	DuEE
Chinese	General	ED EAE	ACE-ZH
Chinese	Financial	ED EAE	FewFC

Models

• Paradigm

  • Token Classification (TC)

  • Sequence Labeling (SL)

  • Sequence to Sequence (Seq2Seq)

  • Machine Reading Comprehension (MRC)

• Backbone

  • CNN / LSTM

  • Transformers (BERT, T5, etc.)

• Aggregation

  • Select [CLS]

  • Dynamic/Max Pooling

  • Marker

  • GCN

• Head

  • Linear / CRF / MRC heads

Contests

OmniEvent plans to support various event extraction contest. Currently, we support the following contests and the list is continually updated!

• MAVEN Event Detection Challenge

• CAIL 2022: Event Detection Track

• LUGE: Information Extraction Track

Experiments

We implement and evaluate state-of-the-art methods on some popular benchmarks using OmniEvent. The results of all Event Detection experiments are shown in the table below. The full results can be accessed via the links below.

• Experiments of base models on All ED Benchmarks

• Experiments of base models on All EAE Benchmarks

• Experiments of All ED Models on ACE-EN+

• Experiments of All EAE Models on ACE-EN+

Tutorials

• Convert the Dataset into Unified OmniEvent Format

Examples

• Examples
• Tuning Large PLMs for Event Extraction

Input Engineering

• Tokenizer
• Whitespace Tokenizer
• Base Processor
• Token Classification Processor
• Sequence Labeling Processor
• Sequence-to-Sequence Processor
• Machine Reading Comprehension (MRC) Converter
• Machine Reading Comprehension (MRC) Processor
• Input Utils

Backbone

• Backbone

Model

• Model
• Label Smoother Sum
• Constraint Decoding

Aggregation

• Aggregation

Classification Head

• Classification Head
• Classification Head
• Conditional Random Field (CRF)

Evaluation

• Evaluation Metrics
• Convert Format
• Dump Results
• Evaluation Utils