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.- Anthology ID:
- D19-3012
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Sebastian Padó, Ruihong Huang
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 67–72
- Language:
- URL:
- https://aclanthology.org/D19-3012
- DOI:
- 10.18653/v1/D19-3012
- Cite (ACL):
- Bo An, Chen Bo, Xianpei Han, and Le Sun. 2019. EUSP: An Easy-to-Use Semantic Parsing PlatForm. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 67–72, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- EUSP: An Easy-to-Use Semantic Parsing PlatForm (An et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D19-3012.pdf