Learning Executable Semantic Parsers for Natural Language Understanding
This addresses the need for more effective natural language understanding systems, but it is incremental as it builds on existing paradigms without introducing a new method.
The paper tackles the problem of learning semantic parsers from data to map natural language into logical forms for question answering and natural language interfaces, highlighting the fusion of logical and statistical approaches as key to future systems.
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.