The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing

Rik van Noord, Johan Bos


Abstract
We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and POS-tagging we gain major improvements in performance compared to a baseline character-level model. Although we improve on previous character-based neural semantic parsing models, the overall accuracy is still lower than a state-of-the-art AMR parser. An ensemble combining our neural semantic parser with an existing, traditional parser, yields a small gain in performance.
Anthology ID:
S17-2160
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
929–933
Language:
URL:
https://www.aclweb.org/anthology/S17-2160
DOI:
10.18653/v1/S17-2160
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2160.pdf