Semantic Parsing: Syntactic assurance to target sentence using LSTM Encoder CFG-Decoder
Fabiano Ferreira Luz, Marcelo Finger

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.
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Speech and dialogue systems
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory
