  Semantic parsing can be defined as the process of mapping natural language
sentences into a machine interpretable, formal representation of its meaning.
Semantic parsing using LSTM encoder-decoder neural networks have become
promising approach. However, human automated translation of natural language
does not provide grammaticality guarantees for the sentences generate such a
guarantee is particularly important for practical cases where a data base query
can cause critical errors if the sentence is ungrammatical. In this work, we
propose an neural architecture called Encoder CFG-Decoder, whose output
conforms to a given context-free grammar. Results are show for any
implementation of such architecture display its correctness and providing
benchmark accuracy levels better than the literature.
