Publication
ACL 2019
Conference paper

Rewarding smatch: Transition-based AMR parsing with reinforcement learning

Download paper

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.

Date

28 Jul 2019

Publication

ACL 2019

Authors

Resources

Share