Challenge: Existing methods to classify events using syntactic dependency relations have not been developed.
Approach: They propose a model which combines syntactic dependency relations with attention-based dynamic tensors to mine node-to-node latent dependency relations via self-attention mechanism.
Outcome: The proposed model improves on the ACE2005 dataset and compares with baseline models.

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Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation (2020.findings-emnlp)

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Challenge: Event detection (ED) is a key subtask of information extraction.
Approach: They propose an architecture that exploits syntactic structure and typed dependency label information to perform event detection.
Outcome: The proposed architecture exploits syntactic structure and typed dependency label information to perform ED.
Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks (2020.emnlp-main)

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Challenge: Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolutional neural networks (GCNs) but the computation of the hidden vectors in such graph-based models is agnostic to the trigger candidate words, leaving irrelevant information for the trigger candidates.
Approach: They propose a mechanism to filter noisy information in the hidden vectors of graph-based models based on the information from the trigger candidate.
Outcome: The proposed model achieves state-of-the-art on two ED datasets.
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks (2021.acl-long)

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Challenge: Existing studies suffer from noise in dependency trees, which can cause confusions in relation extraction.
Approach: They propose a dependency-driven approach for relation extraction with attentive graph convolutional networks . they apply an attention mechanism upon graph convolutional networks to different word dependencies .
Outcome: The proposed approach outperforms previous studies on two English datasets and achieves state-of-the-art performance.
Attention Guided Graph Convolutional Networks for Relation Extraction (P19-1)

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Challenge: Existing approaches for selecting relevant partial dependency structures may not yield optimal results.
Approach: They propose a model which directly takes full dependency trees as inputs and uses them to selectively attend to relevant sub-structures.
Outcome: The proposed model can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task.
Convolutional Self-Attention Networks (N19-1)

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Challenge: Existing models of self-attention networks lack the ability to capture dependencies regardless of distance and can be enhanced with multi-head attention.
Approach: They propose a convolutional self-attention network which can be enhanced by multi-head attention by allowing the model to attend to information from different representation subspaces.
Outcome: The proposed model outperforms existing models on improving locality of SANs on different language pairs and model settings.
Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023.findings-emnlp)

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Challenge: Existing models do not build dependency information among event argument roles . Existing methods do not learn the interactions between different roles based on event structure .
Approach: They propose an intra-event and inter-e event dependency-aware graph network to model dependencies between roles . they use event structure as the fundamental unit to construct role dependencies within events .
Outcome: The proposed model improves on the ACE05, RAMS, and WikiEvents datasets.
Event Detection with Multi-Order Graph Convolution and Aggregated Attention (D19-1)

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Challenge: Existing methods for event detection use first-order syntactic relations to identify trigger words.
Approach: They propose a dependency tree-based method to model and aggregate multi-order syntactic representations in sentences.
Outcome: The proposed method outperforms existing methods on a benchmark dataset . it uses a dependency tree based graph convolution network with aggregative attention .
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (2020.coling-main)

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Challenge: Existing approaches to document-level relation extraction are difficult to establish direct connections between distant entity pairs.
Approach: They propose a global context-enhanced Graph Convolutional Network model which captures rich global context information of entities in a document.
Outcome: The proposed model captures rich global context information of entities in a document.
Relation Extraction with Word Graphs from N-grams (2021.emnlp-main)

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Challenge: Recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to improve performance.
Approach: They propose to use a graph convolutional network to build a context graph without dependency parsers.
Outcome: The proposed approach improves neural RE methods without dependency parsers on English benchmark datasets.
Attention and Edge-Label Guided Graph Convolutional Networks for Named Entity Recognition (2022.emnlp-main)

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Challenge: Named entity recognition (NER) is the recognition of entities with specific meanings in the text, mainly including person, organization, location, etc.
Approach: They propose an edge-aware node joint update module and introduce a node-awful edge update module to explore hidden in structured information and solve the wrong dependency label information to some extent.
Outcome: The proposed model can exploit the structured information on the dependency tree to improve the recognition of long entities.

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