Papers by Tuan Ngo Nguyen
Graph Transformer Networks with Syntactic and Semantic Structures for Event Argument Extraction (2020.findings-emnlp)
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| Challenge: | Existing models for Event Argument Extraction fail to exploit semantic structures of sentences to induce effective representations for EAE. |
| Approach: | They propose a novel model that exploits syntactic and semantic structures of sentences to learn more effective sentence structures for EAE. |
| Outcome: | The proposed model improves the performance of the existing models on standard datasets. |
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. |
On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning (D19-62)
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| Challenge: | Existing models for relation extraction use different pooling mechanisms to perform pooling for RE. |
| Approach: | They conduct a comprehensive study to evaluate the effectiveness of different pooling mechanisms for deep learning in biomedical RE. |
| Outcome: | The proposed model outperforms the previous models on two biomedical datasets. |
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments (2021.emnlp-main)
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| Challenge: | Existing approaches to crosslingual Relation and Event Extraction (REE) suffer from monolingual bias due to training of models on source language data. |
| Approach: | They propose to use unlabeled data in target language to aid alignment of crosslingual representations by fooling a language discriminator. |
| Outcome: | The proposed method significantly advances the state-of-the-art in crosslingual REE tasks. |