Abstract
Semantic parsing aims to map natural language utterances into structured meaning representations. We present a modular platform, EUSP (Easy-to-Use Semantic Parsing PlatForm), that facilitates developers to build semantic parser from scratch. Instead of requiring a large amount of training data or complex grammar knowledge, in our platform developers can build grammar-based semantic parser or neural-based semantic parser through configure files which specify the modules and components that compose semantic parsing system. A high quality grammar-based semantic parsing system only requires domain lexicons rather than costly training data for a semantic parser. Furthermore, we provide a browser-based method to generate the semantic parsing system to minimize the difficulty of development. Experimental results show that the neural-based semantic parser system achieves competitive performance on semantic parsing task, and grammar-based semantic parsers significantly improve the performance of a business search engine.
Highlights
Intelligent applications have been emerging in various forms, such as intelligent retrieval, personal assistants, intelligent customer service robot, etc
One of the core components of these systems is the semantic parser, which maps natural language utterances into formal meaning representations that facilitate the computer to process
The grammar is a set of expert defined rules to compose the semantic units into candidate meaning representations, which is based on the principle of compositionality (Pelletier, 1994)
Summary
Intelligent applications have been emerging in various forms, such as intelligent retrieval, personal assistants, intelligent customer service robot, etc. It is critically desirable to design an easy-to-use platform that facilitates developers to quickly build a high quality semantic parsing system for various domains and applications. Grammar-based semantic parsers employ a set of grammars and lexicons to generate meaning representations for a given utterance. One of the major advantages of neural semantic parsing is that the model is trained in an end-to-end way without requiring the developers to understand the complex theory. Neural semantic parser requires a large amount of training data to achieve competitive performance. It is significantly and crucially desirable to develop a platform for helping developers build semantic parsing systems without requiring complex grammar or costly training data. The grammar-based semantic parser achieves competitive performance without training data, while neural-based semantic parsing is more generalizable. It could produce various formats of outputs, like lambda-calculus and SparQL (Sirin and Parsia, 2007), etc
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