The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing

We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and POS-tagging we gain major improvements in performance compared to a baseline character-level model. Although we improve on previous character-based neural semantic parsing models, the overall accuracy is still lower than a state-of-the-art AMR parser. An ensemble combining our neural semantic parser with an existing, traditional parser, yields a small gain in performance.