Papers with DRE

2 papers
The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction (2020.findings-emnlp)

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Challenge: Existing methods for document-level relation extraction do not capture the representations of the nodes in the graphs.
Approach: They propose to explicitly compute the representations for the nodes in the graph-based edge-oriented model for Document-level Relation Extraction (DRE) . they propose to introduce two novel representation regularization mechanisms to improve the representation vectors for DRE.
Outcome: The proposed model achieves state-of-the-art performance on two benchmark datasets.
Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic (2024.lrec-main)

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Challenge: Existing methods to determine semantic relation between two arguments in dialogues are limited due to the low information density of text.
Approach: They propose a Knowledge-Enhanced Prompt-Tuning method to enhance DRE model by exploiting trigger and label semantics.
Outcome: The proposed method achieves state-of-the-art in F1 and F1c scores on a DialogRE dataset.

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