Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Correlations

Alapfogalmak

Introducing the CARLG framework for improved event argument extraction by incorporating contextual clues and role correlations.

Kivonat

The article introduces the CARLG framework, comprising Contextual Clues Aggregation (CCA) and Role-based Latent Information Guidance (RLIG), to enhance document-level event argument extraction. The CCA module leverages attention weights to assimilate broader contextual information, while the RLIG module captures semantic correlations among event roles. The CARLG framework significantly improves performance with minimal parameter increase across various datasets.

Statisztikák

Our approach introduces less than 1% new parameters.
Comprehensive experiments confirm the superiority of CARLG.
Significant improvements in performance and inference speed are observed.

Idézetek

"Our approach is compatible with all transformer-based event argument extraction methods."
"CARLG introduces less than 1% new parameters yet significantly improves performance."

Főbb Kivonatok

Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction

by Wanlong Liu,... : arxiv.org 03-15-2024

https://arxiv.org/pdf/2310.05116.pdf

Mélyebb kérdések

How does the incorporation of contextual clues impact the overall efficiency of document-level event argument extraction

The incorporation of contextual clues has a significant impact on the overall efficiency of document-level event argument extraction. By leveraging contextual clues, the model can adaptively assimilate broader contextual information specific to each event trigger and argument pair. This allows for a more informed and accurate prediction of complex argument roles by incorporating valuable context information from the surrounding text elements. The Contextual Clues Aggregation (CCA) module enhances candidate argument representation by considering wider context information, leading to superior performance in document-level event argument extraction tasks.

What potential challenges or limitations could arise from integrating role correlations into existing EAE methods

Integrating role correlations into existing Event Argument Extraction (EAE) methods may present potential challenges or limitations. One challenge could be related to the complexity of capturing semantic correlations among different event roles accurately. Ensuring that the model effectively learns and represents these relationships without introducing noise or bias is crucial for successful integration. Additionally, incorporating role correlations may require additional computational resources and training data to capture nuanced interactions between various roles accurately.
Another limitation could arise from the interpretability of extracted arguments when integrating role correlations. While latent role embeddings enhance semantic interaction among event roles, interpreting these embeddings and understanding how they contribute to the final predictions may pose challenges for users or stakeholders who need transparent explanations for decision-making processes based on extracted arguments.

How might the utilization of latent role embeddings influence the interpretability of extracted arguments

The utilization of latent role embeddings can significantly influence the interpretability of extracted arguments in document-level event argument extraction tasks. By incorporating latent role embeddings through Role-based Latent Information Guidance (RLIG), the model captures semantic correlations among different event roles effectively.
This enhanced representation not only improves performance but also provides valuable insights into how different roles interact within an event context. The use of latent role embeddings enables a deeper understanding of the relationships between various roles, making it easier to interpret why certain arguments are associated with specific events or triggers.
Overall, utilizing latent role embeddings enhances both performance and interpretability in extracting arguments at a document level by providing richer representations that capture intricate semantic connections among different roles within an event context.