Papers › An AMR Aligner Tuned by Transition-based Parser
An AMR Aligner Tuned by Transition-based Parser
Yijia Liu, Wanxiang Che, Bo Zheng, Bing Qin, Ting Liu
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| AMR Parsing | LDC2014T12 | Transition-based+improved aligner+ensemble | F1 Full | 68.4 | #6 of 12 | Archive leaderboard | report |
| AMR Parsing | LDC2014T12 | Transition-based+improved aligner+ensemble | F1 Newswire | 73.3 | #6 of 12 | Archive leaderboard | report |
| AMR Parsing | LDC2014T12: | Transition-based+improved aligner+ensemble | F1 Full | 0.68 | #2 of 5 | Archive leaderboard | report |
| AMR Parsing | LDC2014T12: | Transition-based+improved aligner+ensemble | F1 Newswire | 0.73 | #2 of 5 | Archive leaderboard | report |
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