Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Table 1: SMATCH scores for AMR Parsing. *Reported numbers are on the newswire portion of a previous release of the corpus (LDC2014T12).
['Model', 'Dev Prec', 'Dev Rec', 'Dev F1', 'Test Prec', 'Test Rec', 'Test F1']
unlabeled Gigaword sentences. Through every round of self-training, our parser improves. Our final parser outperforms comparable seq2seq and character LSTM models by over 10 points. While much of this improvement comes from self-training, our model without Gigaword data outperforms these approaches by 3.5 points on F1. We attribute this increase in performance to different handling of preprocessing and more careful hyper-parameter tuning. All other models that we compare against use semantic resources, such as WordNet, dependency parsers or CCG parsers (models marked with * were trained with less data, but only evaluate on newswire text; the rest evaluate on the full test set, containing text from blogs). Our full models outperform the original version of JAMR Flanigan et al.