Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, Miguel Ballesteros
Abstract
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings. We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser.- Anthology ID:
- P19-1451
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4586–4592
- Language:
- URL:
- https://aclanthology.org/P19-1451
- DOI:
- 10.18653/v1/P19-1451
- Cite (ACL):
- Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, and Miguel Ballesteros. 2019. Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4586–4592, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (Naseem et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/P19-1451.pdf
- Data
- LDC2017T10