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
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 929–933
- Language:
- URL:
- https://aclanthology.org/S17-2160
- DOI:
- 10.18653/v1/S17-2160
- Cite (ACL):
- Rik van Noord and Johan Bos. 2017. The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 929–933, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing (van Noord & Bos, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/S17-2160.pdf
- Data
- Bio