Compositional Semantic Parsing across Graphbanks

Matthias Lindemann, Jonas Groschwitz, Alexander Koller


Abstract
Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.
Anthology ID:
P19-1450
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4576–4585
Language:
URL:
https://www.aclweb.org/anthology/P19-1450
DOI:
10.18653/v1/P19-1450
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1450.pdf
Poster:
 P19-1450.Poster.pdf