Semantic Parsing: Syntactic assurance to target sentence using LSTM Encoder CFG-Decoder
This addresses the need for grammatical guarantees in practical applications like database queries to prevent critical errors, representing an incremental improvement over existing methods.
The paper tackles the problem of ensuring grammatical correctness in semantic parsing by proposing an Encoder CFG-Decoder architecture that outputs sentences conforming to a context-free grammar, achieving benchmark accuracy levels better than existing literature.
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.