On the Role of Syntactic Graph Convolutions for Identifying and Classifying Argument Components

AAAI 2019 pp. 9997-9998

doi:10.1609/AAAI.V33I01.33019997 /aaai/2019/morio2019aaai-role/

Abstract

This paper focuses on fundamental research that combines syntactic knowledge with neural studies, which utilize syntactic information in argument component identification and classification (AC-I/C) tasks in argument mining (AM). The following are our paper’s contributions: 1) We propose a way of incorporating a syntactic GCN into multi-task learning models for AC-I/C tasks. 2) We demonstrate the valid effectiveness of our proposed syntactic GCN in fair experiments in some datasets. We also found that syntactic GCNs are promising for lexically independent scenarios. Our code in the experiments is available for reproducibility.1

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Cite

Text

Morio and Fujita. "On the Role of Syntactic Graph Convolutions for Identifying and Classifying Argument Components." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019997

Markdown

[Morio and Fujita. "On the Role of Syntactic Graph Convolutions for Identifying and Classifying Argument Components." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/morio2019aaai-role/) doi:10.1609/AAAI.V33I01.33019997

BibTeX

@inproceedings{morio2019aaai-role,
  title     = {{On the Role of Syntactic Graph Convolutions for Identifying and Classifying Argument Components}},
  author    = {Morio, Gaku and Fujita, Katsuhide},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {9997-9998},
  doi       = {10.1609/AAAI.V33I01.33019997},
  url       = {https://mlanthology.org/aaai/2019/morio2019aaai-role/}
}