Semantic Parsing: Syntactic assurance to target sentence using LSTM Encoder CFG-Decoder

TL;DR

This paper introduces Encoder CFG-Decoder, a neural network architecture that ensures generated semantic parses conform to a specified grammar, improving correctness and reliability over existing methods.

Contribution

It proposes a novel neural architecture that guarantees grammaticality of semantic parses by integrating context-free grammar constraints into the decoder.

Findings

Achieves higher accuracy than existing semantic parsing methods.

Ensures grammatical correctness of generated outputs.

Demonstrates the effectiveness of grammar-constrained neural decoding.

Abstract

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.