Abstract
In this paper, we propose a new rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. Our aligner is further tuned by our oracle parser via picking the alignment that leads to the highest-scored achievable AMR graph. Experimental results show that our aligner outperforms the rule-based aligner in previous work by achieving higher alignment F1 score and consistently improving two open-sourced AMR parsers. Based on our aligner and transition system, we develop a transition-based AMR parser that parses a sentence into its AMR graph directly. An ensemble of our parsers with only words and POS tags as input leads to 68.4 Smatch F1 score, which outperforms the current state-of-the-art parser.- Anthology ID:
- D18-1264
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2422–2430
- Language:
- URL:
- https://aclanthology.org/D18-1264
- DOI:
- 10.18653/v1/D18-1264
- Cite (ACL):
- Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, and Ting Liu. 2018. An AMR Aligner Tuned by Transition-based Parser. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2422–2430, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- An AMR Aligner Tuned by Transition-based Parser (Liu et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D18-1264.pdf
- Code
- Oneplus/tamr