Abstract
We present a novel rule-based system for automatic generation of factual questions from sentences, using semantic role labeling (SRL) as the main form of text analysis. The system is capable of generating both wh-questions and yes/no questions from the same semantic analysis. We present an extensive evaluation of the system and compare it to a recent neural network architecture for question generation. The SRL-based system outperforms the neural system in both average quality and variety of generated questions.- Anthology ID:
- W18-0530
- Volume:
- Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 254–263
- Language:
- URL:
- https://aclanthology.org/W18-0530
- DOI:
- 10.18653/v1/W18-0530
- Cite (ACL):
- Michael Flor and Brian Riordan. 2018. A Semantic Role-based Approach to Open-Domain Automatic Question Generation. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 254–263, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- A Semantic Role-based Approach to Open-Domain Automatic Question Generation (Flor & Riordan, BEA 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-0530.pdf
- Data
- SQuAD