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• W2890996951 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." @default.
• W2890996951 created "2018-09-27" @default.
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• W2890996951 date "2018-01-01" @default.
• W2890996951 modified "2023-09-27" @default.
• W2890996951 title "An AMR Aligner Tuned by Transition-based Parser" @default.
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• W2890996951 doi "https://doi.org/10.18653/v1/d18-1264" @default.
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