Robust Incremental Neural Semantic Graph Parsing
Table 1: DMRS development set results for attention-based encoder-decoder models with alignments encoded in the linearization, for top-down (TD) and arc-eager (AE) linearizations, and lexicalized and unlexicalized predicate prediction.
['Model', 'EDM', 'EDM [ITALIC] P', 'EDM [ITALIC] A']
First we report results using standard attention-based encoder-decoders, with the alignments encoded as token strings in the linearization. We compare the top-down (TD) and arc-eager (AE) linearizations, as well as the effect of delexicalizing the predicates (factorizing lemmas out of the linearization and predicting them separately.) In both cases constants are predicted with a dictionary lookup based on the predicted spans. A special label is predicted for predicates not in the ERG lexicon – the words and POS tags that make up those predicates are recovered through the alignments during post-processing.