@inproceedings{dutta-etal-2021-gtn,
title = "{GTN}-{ED}: Event Detection Using Graph Transformer Networks",
author = "Dutta, Sanghamitra and
Ma, Liang and
Saha, Tanay Kumar and
Liu, Di and
Tetreault, Joel and
Jaimes, Alejandro",
editor = "Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Logacheva, Varvara and
Jana, Abhik and
Ustalov, Dmitry and
Jansen, Peter",
booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.textgraphs-1.13",
doi = "10.18653/v1/2021.textgraphs-1.13",
pages = "132--137",
abstract = "Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection. However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous. In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Network (GTN). We integrate GTN to leverage dependency relations on two existing homogeneous-graph-based models and demonstrate an improvement in the F1 score on the ACE dataset.",
}
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%0 Conference Proceedings
%T GTN-ED: Event Detection Using Graph Transformer Networks
%A Dutta, Sanghamitra
%A Ma, Liang
%A Saha, Tanay Kumar
%A Liu, Di
%A Tetreault, Joel
%A Jaimes, Alejandro
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Logacheva, Varvara
%Y Jana, Abhik
%Y Ustalov, Dmitry
%Y Jansen, Peter
%S Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F dutta-etal-2021-gtn
%X Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection. However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous. In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Network (GTN). We integrate GTN to leverage dependency relations on two existing homogeneous-graph-based models and demonstrate an improvement in the F1 score on the ACE dataset.
%R 10.18653/v1/2021.textgraphs-1.13
%U https://aclanthology.org/2021.textgraphs-1.13
%U https://doi.org/10.18653/v1/2021.textgraphs-1.13
%P 132-137
Markdown (Informal)
[GTN-ED: Event Detection Using Graph Transformer Networks](https://aclanthology.org/2021.textgraphs-1.13) (Dutta et al., TextGraphs 2021)
ACL
- Sanghamitra Dutta, Liang Ma, Tanay Kumar Saha, Di Liu, Joel Tetreault, and Alejandro Jaimes. 2021. GTN-ED: Event Detection Using Graph Transformer Networks. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15), pages 132–137, Mexico City, Mexico. Association for Computational Linguistics.