Papers › Compositional Semantic Parsing Across Graphbanks

Compositional Semantic Parsing Across Graphbanks

27 Jun 2019ACL 2019 7arXiv:1906.11746archive 2025-07-28

Matthias Lindemann, Jonas Groschwitz, Alexander Koller

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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Multi-Task Learning Semantic Parsing

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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