Abstract
This paper describes the Jeonbuk National University (JBNU) system for the 2020 shared task on Cross-Framework Meaning Representation Parsing at the Conference on Computational Natural Language Learning. Among the five frameworks, we address only the abstract meaning representation framework and propose a joint state model for the graph-sequence iterative inference of (Cai and Lam, 2020) for a simplified graph-sequence inference. In our joint state model, we update only a single joint state vector during the graph-sequence inference process instead of keeping the dual state vectors, and all other components are exactly the same as in (Cai and Lam, 2020).- Anthology ID:
- 2020.conll-shared.8
- Volume:
- Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Stephan Oepen, Omri Abend, Lasha Abzianidze, Johan Bos, Jan Hajič, Daniel Hershcovich, Bin Li, Tim O'Gorman, Nianwen Xue, Daniel Zeman
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 83–87
- Language:
- URL:
- https://aclanthology.org/2020.conll-shared.8
- DOI:
- 10.18653/v1/2020.conll-shared.8
- Cite (ACL):
- Seung-Hoon Na and Jinwoo Min. 2020. JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference. In Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing, pages 83–87, Online. Association for Computational Linguistics.
- Cite (Informal):
- JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference (Na & Min, CoNLL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.conll-shared.8.pdf