An AMR Aligner Tuned by Transition-based Parser
Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, Ting Liu
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- github.com/Oneplus/tamrOfficialIn papernone★ 0
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| LDC2014T12 | Transition-based+improved aligner+ensemble | F1 Full | 68.4 | — | Unverified |
| LDC2014T12 | Transition-based+improved aligner+ensemble | F1 Newswire | 0.73 | — | Unverified |