Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

Tahira Naseem, Abhishek Shah, Hui Wan, Radu Florian, Salim Roukos, Miguel Ballesteros

2019-05-31ACL 2019 7Reinforcement LearningAMR Parsingreinforcement-learning
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Abstract

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

Results

TaskDatasetMetricValueModel
Semantic ParsingLDC2017T10Smatch73.4Rewarding Smatch (IBM)
AMR ParsingLDC2017T10Smatch73.4Rewarding Smatch (IBM)

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