RIGA at SemEval-2016 task 8: Impact of smatch extensions and character-level neural translation on AMR parsing accuracy

Abstract

Two extensions to the AMR smatch scoring script are presented. The first extension combines the smatch scoring script with the C6.0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs. This first extension results in 4% gain over the state-of-art CAMR baseline parser by adding to it a manually crafted wrapper fixing the identified CAMR parser errors. The second extension combines a per-sentence smatch with an ensemble method for selecting the best AMR graph among the set of AMR graphs for the same sentence. This second modification automatically yields further 0.4% gain when applied to outputs of two nondeterministic AMR parsers: a CAMR+wrapper parser and a novel character-level neural translation AMR parser. For AMR parsing task the character-level neural translation attains surprising 7% gain over the carefully optimized word-level neural translation. Overall, we achieve smatch F1=62% on the SemEval-2016 official scoring set and F1=67% on the LDC2015E86 test set.