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2019 ACL ACL 2019

Compositional Semantic Parsing across Graphbanks

Abstract

AbstractMost 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — compositional parsing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Matthias Lindemann , Jonas Groschwitz , Alexander Koller

Topics

Artificial Intelligence > Core AI > Multimodal Learning Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Learning Types > Semi-Supervised Learning Natural Language Processing > Applications > Semantic Parsing Deep Learning > Learning Types > Multi-Task Learning

Keywords

multi-task learning semantic parsing bert embedding neural network compositional parsing
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