Efficient Document-Level Multi-Event Argument Extraction with Dependency-Guided Encoding and Event-Specific Information Aggregation

Core Concepts

A novel multi-event argument extraction model DEEIA that can efficiently extract arguments for all events within a document simultaneously, by employing a dependency-guided encoding module and an event-specific information aggregation module.

Abstract

This paper proposes a multi-event argument extraction (Multi-EAE) model called DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation) that can efficiently extract arguments for all events within a document simultaneously.
The key highlights are:

The DEEIA model is built upon the state-of-the-art prompt-based single-event argument extraction (Single-EAE) model PAIE, and introduces a multi-event prompt mechanism to enable extracting arguments from multiple events at once.

To tackle the challenge of increased information complexity when processing multiple events, the DEEIA model employs two novel modules:

Dependency-guided Encoding (DE) module: Guides the model to associate the multi-event prompts with their corresponding event contexts using pre-defined event dependencies.
Event-specific Information Aggregation (EIA) module: Adaptively aggregates useful context information relevant to the specific event and argument being extracted.

Extensive experiments on four public datasets (RAMS, WikiEvents, MLEE, ACE05) show that the DEEIA model outperforms major benchmarks in both performance and inference time. Further analyses demonstrate the effectiveness of the proposed DE and EIA modules.

The DEEIA model significantly improves the efficiency of document-level EAE tasks, saving up to 69.19% inference time compared to the baseline multi-EAE method TabEAE-multi, with almost no increase in the number of parameters.

The DEEIA model exhibits stronger performance on multi-event instances compared to single-event baselines, indicating its effectiveness in capturing beneficial event correlations.

Stats

The government has taken back a number of areas with starve-or-surrender tactics, bombarding and starving people until they agree to leave.
Dzhokhar Tsarnaev visits Silva and borrows the Ruger pistol, which was later used to kill MIT police officer Sean Collier.
During the shootout with police in Watertown, the gun was used.

Quotes

"Prosecutors said these items were used to help remotely - detonate the bombs February , 2013 Dzhokhar Tsarnaev visits Silva and borrows the Ruger pistol — the gun that was later used to kill MIT police officer Sean Collier and during the shootout with police in Watertown."

Key Insights Distilled From

Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction

by Wanlong Liu,... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.01884.pdf

Deeper Inquiries

How can the DEEIA model be extended to handle even longer input texts without sacrificing performance?

To handle longer input texts without sacrificing performance, the DEEIA model can be extended by implementing more advanced techniques for processing long sequences. One approach could be to incorporate hierarchical modeling, where the input text is divided into segments or chunks, and the model processes these segments sequentially. This hierarchical approach allows the model to focus on relevant parts of the text while maintaining context across different segments. Additionally, the model can utilize techniques like sparse attention mechanisms or memory-efficient architectures to reduce the computational burden of processing long sequences. By efficiently managing memory and attention, the DEEIA model can effectively handle longer input texts without compromising performance.

What other types of event correlations, beyond the intra-event and inter-event dependencies explored in this work, could be leveraged to further improve the multi-event argument extraction task?

In addition to intra-event and inter-event dependencies, other types of event correlations can be leveraged to further improve the multi-event argument extraction task. One potential type of correlation is temporal dependencies, where events are linked based on their temporal order or sequence in the document. By considering the temporal relationships between events, the model can better understand the flow of events and extract arguments more accurately. Another type of correlation is causal relationships, where events are connected based on cause-and-effect patterns. By capturing causal dependencies between events, the model can infer implicit arguments and roles more effectively. Furthermore, thematic correlations, where events share common themes or topics, can also be leveraged to enhance argument extraction by identifying recurring patterns or motifs across events.

Given the model's ability to capture event correlations, how could the DEEIA framework be adapted to tackle other related tasks, such as joint event extraction or document-level relation extraction?

The DEEIA framework's ability to capture event correlations can be leveraged to tackle other related tasks such as joint event extraction or document-level relation extraction by extending the model architecture and training objectives. For joint event extraction, the DEEIA model can be modified to simultaneously extract multiple types of events within a document by incorporating multi-task learning techniques. By jointly optimizing for different event types, the model can leverage the correlations between events to improve overall extraction performance.
For document-level relation extraction, the DEEIA framework can be adapted to identify and extract complex relationships between entities and events in a document. By incorporating relation-specific prompts and encoding mechanisms, the model can capture the nuanced interactions between entities and events, enabling more comprehensive relation extraction. Additionally, the DEEIA model can be fine-tuned on datasets specifically annotated for document-level relations to enhance its ability to extract and understand complex relational structures within text.