Deep neural semantic parsing: translating from natural language into SPARQL

Abstract

This is the original version of the thesis written by the candidate Fabiano Ferreira Luz submitted to the Judging Committee.I would like to thank my advisor Marcelo Finger for allowing me to explore new approaches to semantic parsing, for his support and constructive criticism of my work. I would like to thank all my friends and family who have been on my side during the development of this work. There are so many friends and loved ones that I will not even try to name just to not commit the rudeness of forgetting someone.iii iv Abstract Semantic parsing is the process of mapping a natural-language sentence into a machine-readable, formal representation of its meaning. The LSTM Encoder-Decoder is a neural architecture with the ability to map a source language into a target one. We are interested in the problem of mapping natural language into SPARQL queries, and we seek to contribute with strategies that do not rely on handcrafted rules, high-quality lexicons, manually-built templates or other handmade complex structures. In this context, we present two contributions to the problem of semantic parsing departing from the LSTM encoder-decoder. While natural language has well defined vector representation methods that use a very large volume of texts, formal languages, like SPARQL queries, suffer from lack of suitable methods for vector representation. In the first contribution we improve the representation of SPARQL vectors. We start by obtaining an alignment matrix between the two vocabularies, natural language and SPARQL terms, which allows us to refine a vectorial representation of SPARQL items. With this refinement we obtained better results in the posterior training for the semantic parsing model. In the second contribution we propose a neural architecture, that we call Encoder CFG-Decoder, whose output conforms to a given context-free grammar. Unlike the traditional LSTM encoder-decoder, our model provides a grammatical guarantee for the mapping process, which is particularly important for practical cases where grammatical errors can cause critical failures. Results confirm that any output generated by our model obeys the given CFG, and we observe a translation accuracy improvement when compared with other results from the literature. ResumoA análise semântica é o processo de mapear uma sentença em linguagem natural para uma representação formal, interpretável por máquina, do seu significado. O LSTM Encoder-Decoder é uma arquitetura de rede neural com a capacidade de mapear uma sequência de origem para uma sequência de destino. Estamos interessados no problema de mapear a linguagem natural em consultas SPARQL e procuramos contribuir com estratégias que não dependam de regras artesanais, léxico de alta qualidade, modelos construídos manualmente ou outras estruturas complexas feitas à mão.Neste contexto, apresentamos duas contribuições para o problema de análise semântica partindo da arquitetura LSTM Encoder-Decoder. Enquanto para a linguagem natural existem métodos de representação vetorial bem definidos que u...

Toward Development of A.D.A. – Advanced Distributed Assistant

“…[Roman 2001] is one that does this based on the works of [Grosz and Sidner 1986] along multi-agent theory. [Luz 2019] based on LSTM encoder-decoder network architecture translates from natural language to SPARQL, an data retrieval formal language; • Text-to-speech Synthesis: speech synthesis is a fundamental piece in the voice interaction loop between user and machine in a virtual assistant, as it is responsible for providing appropriate user feedback, given a certain uttering. Knowledge about processing and manipulating this interface is required in order to adapt or even propose new voice interaction models.…”