An AMR Aligner Tuned by Transition-based Parser

Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, Ting Liu


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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/D18-1264.pdf
Video:
 https://vimeo.com/306049123
Code
 Oneplus/tamr