Broad-Coverage Semantic Parsing as Transduction

Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme

Abstract
We unify different broad-coverage semantic parsing tasks into a transduction parsing paradigm, and propose an attention-based neural transducer that incrementally builds meaning representation via a sequence of semantic relations. By leveraging multiple attention mechanisms, the neural transducer can be effectively trained without relying on a pre-trained aligner. Experiments separately conducted on three broad-coverage semantic parsing tasks – AMR, SDP and UCCA – demonstrate that our attention-based neural transducer improves the state of the art on both AMR and UCCA, and is competitive with the state of the art on SDP.
Anthology ID:
D19-1392
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3786–3798
Language:
URL:
https://aclanthology.org/D19-1392
DOI:
10.18653/v1/D19-1392
Bibkey:
Cite (ACL):
Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019. Broad-Coverage Semantic Parsing as Transduction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3786–3798, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Broad-Coverage Semantic Parsing as Transduction (Zhang et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/D19-1392.pdf
Data
LDC2017T10, Universal Dependencies
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