Abstract
We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.- Anthology ID:
- D17-1130
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1269–1275
- Language:
- URL:
- https://aclanthology.org/D17-1130
- DOI:
- 10.18653/v1/D17-1130
- Cite (ACL):
- Miguel Ballesteros and Yaser Al-Onaizan. 2017. AMR Parsing using Stack-LSTMs. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1269–1275, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- AMR Parsing using Stack-LSTMs (Ballesteros & Al-Onaizan, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D17-1130.pdf