Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

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 of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings... We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser read more

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
AMR Parsing LDC2017T10 Naseem et al. Smatch 73.4 # 9

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