Learning Executable Semantic Parsers for Natural Language Understanding

TL;DR

This paper discusses the development of statistical semantic parsers that convert natural language into logical forms, emphasizing their importance in question answering and language understanding systems.

Contribution

It provides an overview of the components and challenges of learning semantic parsers from data, highlighting the fusion of logical and statistical methods.

Findings

Semantic parsing is crucial for natural language understanding.

Learning from data introduces key statistical and computational challenges.

Semantic parsing combines logical and statistical approaches.

Abstract

For building question answering systems and natural language interfaces, semantic parsing has emerged as an important and powerful paradigm. Semantic parsers map natural language into logical forms, the classic representation for many important linguistic phenomena. The modern twist is that we are interested in learning semantic parsers from data, which introduces a new layer of statistical and computational issues. This article lays out the components of a statistical semantic parser, highlighting the key challenges. We will see that semantic parsing is a rich fusion of the logical and the statistical world, and that this fusion will play an integral role in the future of natural language understanding systems.