Papers by Hieu Minh Tran

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.
Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution (2021.acl-long)

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Challenge: Existing deep learning models for event coreference resolution are limited in that they cannot exploit important interactions between relevant objects for ECR.
Approach: They propose a deep learning model that groups coreferent event mentions into the same clusters . they use document structures to capture relevant objects for ECR .
Outcome: The proposed model achieves state-of-the-art on two benchmark datasets.

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