Eight NMT models are evaluated for natural language to SPARQL translation, with CNN-based models reaching BLEU up to 98 and accuracy up to 94% on high-quality datasets.
Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention
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abstract
Semantic parsing is the process of mapping a natural language sentence into a formal representation of its meaning. In this work we use the neural network approach to transform natural language sentence into a query to an ontology database in the SPARQL language. This method does not rely on handcraft-rules, high-quality lexicons, manually-built templates or other handmade complex structures. Our approach is based on vector space model and neural networks. The proposed model is based in two learning steps. The first step generates a vector representation for the sentence in natural language and SPARQL query. The second step uses this vector representation as input to a neural network (LSTM with attention mechanism) to generate a model able to encode natural language and decode SPARQL.
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cs.CL 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Neural Machine Translating from Natural Language to SPARQL
Eight NMT models are evaluated for natural language to SPARQL translation, with CNN-based models reaching BLEU up to 98 and accuracy up to 94% on high-quality datasets.