Exploiting Contextual Information via Dynamic Memory Network for Event Detection (D18-1)
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| Challenge: | Existing methods for event detection only process context once . a multi-hop mechanism to capture contextual information improves performance . |
| Approach: | They propose to use dynamic memory network to capture contextual information . they propose to model event triggers by identifying word or phrase which most represents it . |
| Outcome: | The proposed model achieves best F1 score compared to the state-of-the-art models. |
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| Challenge: | Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies . |
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Dynamic Global Memory for Document-level Argument Extraction (2022.acl-long)
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| Challenge: | Recent work on document-level event argument extraction is restricted by sequence length constraints and ignores global context between events. |
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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. |
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| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
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Incremental Event Detection via Knowledge Consolidation Networks (2020.emnlp-main)
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| Challenge: | Existing approaches to event detection require a fixed set of pre-defined event types . existing methods cannot handle semantic ambiguity and training data imbalance problems . |
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ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (D18-1)
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Event Detection as Graph Parsing (2021.findings-acl)
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| Challenge: | Existing approaches to event detection focus on using syntactic dependency structures or external knowledge to boost the performance. |
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Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems (P18-1)
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| Challenge: | End-to-end task-oriented dialog systems often suffer from the challenge of incorporating knowledge bases. |
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