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

2018/07/18 by Fabiano Ferreira Luz, Luz, Fabiano Ferreira, Marcelo Finger +1
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1807.07108

openalex publication_date 2018/07/18 · openalex created_date 2018/08/03 · openalex updated_date 2026/07/28

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.

Citations

• Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
• Language to Logical Form with Neural Attention

Related

• Compositional Neural Machine Translation by Removing the Lexicon from Syntax
• Coarse-to-Fine Decoding for Neural Semantic Parsing
• Robust Incremental Neural Semantic Graph Parsing
• A Globally Normalized Neural Model for Semantic Parsing
• A Deep Architecture for Semantic Parsing