REXEL: An Efficient and Accurate End-to-End Model for Document-Level Closed Information Extraction

Core Concepts

REXEL is a highly efficient and accurate end-to-end model that extracts structured facts fully linked to a reference knowledge graph from unstructured text at the document level in a single forward pass.

Abstract

The paper introduces REXEL, a novel end-to-end model for document-level closed information extraction (DocIE). REXEL jointly performs the following subtasks in a single forward pass:

Mention Detection (MD): Extracting mention spans from the input text.
Entity Typing (ET): Predicting the entity types for the extracted mentions.
Coreference Resolution (Coref): Clustering mentions referring to the same entity.
Relation Classification (RC): Extracting relations between entity pairs at the document level.
Entity Disambiguation (ED): Linking the extracted entity mentions to a reference knowledge graph.

The key highlights of REXEL are:

It addresses the limitations of existing pipeline approaches for closed information extraction, which are prone to error propagation and restricted to sentence-level extraction.
REXEL's modular architecture allows it to be deployed for various combinations of the subtasks, providing flexibility and efficiency.
REXEL outperforms state-of-the-art baselines on the individual subtasks by an average of 6 F1 points and on the end-to-end relation extraction task by over 6 F1 points.
REXEL is on average 11 times faster than the baselines in the end-to-end relation extraction setting, making it suitable for web-scale applications.
The authors release an extension of the DocRED dataset, named DocRED-IE, to enable benchmarking of future work on document-level closed information extraction.

Stats

40.7% of the facts in a document can only be determined at the document level.
10 relations account for about 60% of the facts in DocRED.
The 10 most frequent relations account for more than 75% of the facts in DWIE.

Quotes

"Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE)."
"REXEL performs mention detection, entity typing, entity disambiguation, coreference resolution and document-level relation classification in a single forward pass to yield facts fully linked to a reference knowledge graph."
"REXEL is on average 11 times faster than competitive existing approaches in a similar setting and performs competitively both when optimised for any of the individual sub-tasks and a variety of combinations of different joint tasks, surpassing the baselines by an average of more than 6 F1 points."

Key Insights Distilled From

REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking

by Nacime Bouzi... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12788.pdf

Deeper Inquiries

How can REXEL's modular architecture be leveraged to enable efficient transfer learning across different closed information extraction tasks

REXEL's modular architecture can be leveraged to enable efficient transfer learning across different closed information extraction tasks by allowing for easy adaptation and reusability of individual components. Each subtask in REXEL, such as mention detection, entity typing, coreference resolution, relation classification, and entity disambiguation, is designed as a separate module within the architecture. This modular design enables the isolation of specific functionalities, making it easier to fine-tune or replace individual components without affecting the entire system.
For transfer learning, one can leverage the pre-trained modules of REXEL and fine-tune them on a new dataset or task. By freezing certain modules and only updating the parameters of relevant components, the model can quickly adapt to new tasks while retaining the knowledge learned from the original training. This approach not only saves computational resources but also speeds up the training process for new tasks.
Furthermore, the modular architecture of REXEL allows for easy experimentation with different combinations of subtasks. Researchers can explore various joint task settings by selectively including or excluding specific modules, thereby customizing the model for different closed information extraction tasks. This flexibility in configuration enables efficient transfer learning across a wide range of tasks while maintaining high performance and accuracy.

What are the potential limitations of the DocIE hard metric, and how could it be extended or modified to better align with real-world application requirements

The DocIE hard metric, while providing a standardized evaluation framework for document-level closed information extraction, may have certain limitations that could impact its alignment with real-world application requirements. Some potential limitations of the DocIE hard metric include:

Strict Cluster Evaluation: The hard metric penalizes entire clusters if any mention within the cluster is incorrectly linked or identified. This strict criterion may not always reflect the practical importance of individual errors, especially in scenarios where missing a few mentions within an entity cluster may not significantly impact downstream applications.

Entity Identifier Dependency: The metric heavily relies on the correctness of entity identifiers for cluster evaluation. In real-world scenarios, entity linking may introduce errors or inconsistencies that could affect the overall performance assessment, especially when dealing with noisy or ambiguous data.

To address these limitations and better align the DocIE hard metric with real-world application requirements, the metric could be extended or modified in the following ways:

Soft Cluster Evaluation: Introduce a soft clustering evaluation approach that considers partial correctness within clusters. Instead of penalizing entire clusters for a single error, assign partial scores based on the proportion of correctly linked mentions within a cluster. This approach provides a more nuanced assessment of cluster quality.

Error Weighting: Implement a weighted evaluation scheme that assigns different weights to errors based on their impact on downstream tasks. For example, errors in core entities could be weighted more heavily than errors in peripheral entities to reflect their relative importance.

Task-Specific Metrics: Develop task-specific evaluation metrics that capture the specific requirements and constraints of different applications. Customized metrics can provide a more tailored assessment of model performance in contextually relevant scenarios.

By incorporating these modifications, the DocIE hard metric can be enhanced to better reflect the complexities and nuances of real-world applications, ensuring that model evaluations align more closely with practical use cases.

Given the significant class imbalance observed in the datasets, how could REXEL's training be further improved to enhance its robustness to rare relations and entities

To enhance REXEL's training robustness to rare relations and entities due to significant class imbalance in the datasets, several strategies can be implemented:

Data Augmentation: Augmenting the training data by introducing synthetic examples or perturbing existing samples can help balance the class distribution and provide the model with more exposure to rare relations and entities.

Class Weighting: Assigning higher weights to rare classes during training can help the model prioritize learning from underrepresented samples, improving its ability to generalize to rare entities and relations.

Ensemble Learning: Employing ensemble learning techniques by training multiple models with different initializations or architectures and combining their predictions can enhance the model's ability to capture rare patterns and improve overall performance.

Active Learning: Implementing active learning strategies to selectively query and label instances that are most informative or challenging for the model can focus training on rare classes, leading to better generalization and performance on underrepresented entities and relations.

Fine-tuning with Synthetic Data: Generating synthetic data specifically designed to represent rare entities and relations can be used for fine-tuning the model, providing additional exposure to these classes and improving performance on rare instances.

By incorporating these strategies into REXEL's training pipeline, the model can become more robust to class imbalances and better equipped to handle rare entities and relations in the datasets, ultimately enhancing its overall performance and accuracy.