most citedRewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

13 citations · 13 across the 1 of their papers we have counts for

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1 paper

cs.CL2019★ 13 cited

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

Tahira Naseem, Abhishek Shah, Hui Wan +3

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score…