arXiv:2009.07365
공식 코드 · Papers with Code
Fast semantic parsing with well-typedness guarantees
원문 초록
AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers slow. We describe an A* parser and a transition-based parser for AM dependency parsing which guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude, while maintaining or improving accuracy.
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Where, What, Why: Towards Explainable Driver Attention Prediction
Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Hierarchical Bracketing Encodings for Dependency Parsing as Tagging
Sigma: A dataset for text-to-code semantic parsing with statistical analysis
Diverse In-Context Example Selection After Decomposing Programs and Aligned Utterances Improves Semantic Parsing
An Aspect Extraction Framework using Different Embedding Types, Learning Models, and Dependency Structure
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