Abstract
Second-order neural parsers have obtained high accuracy in semantic dependency parsing. Inspired by the factor graph representation of second-order parsing, we propose edge graph neural networks (E-GNNs). In an E-GNN, each node corresponds to a dependency edge, and the neighbors are defined in terms of sibling, co-parent, and grandparent relationships. We conduct experiments on SemEval 2015 Task 18 English datasets, showing the superior performance of E-GNNs.- Anthology ID:
- 2022.findings-emnlp.452
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6096–6102
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.452
- DOI:
- Cite (ACL):
- Songlin Yang and Kewei Tu. 2022. Semantic Dependency Parsing with Edge GNNs. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6096–6102, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Semantic Dependency Parsing with Edge GNNs (Yang & Tu, Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.findings-emnlp.452.pdf