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Compositional Semantic Parsing Across Graphbanks

2019-06-27ACL 2019Code Available0· sign in to hype

Matthias Lindemann, Jonas Groschwitz, Alexander Koller

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Abstract

Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks. Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.

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