ParaQG: A System for Generating Questions and Answers from Paragraphs

Vishwajeet Kumar, Sivaanandh Muneeswaran, Ganesh Ramakrishnan, Yuan-Fang Li


Abstract
Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications. Manual generation is a labour-intensive task, as it requires the reading, parsing and understanding of long passages of text. A number of question generation models based on sequence-to-sequence techniques have recently been proposed. Most of them generate questions from sentences only, and none of them is publicly available as an easy-to-use service. In this paper, we demonstrate ParaQG, a Web-based system for generating questions from sentences and paragraphs. ParaQG incorporates a number of novel functionalities to make the question generation process user-friendly. It provides an interactive interface for a user to select answers with visual insights on generation of questions. It also employs various faceted views to group similar questions as well as filtering techniques to eliminate unanswerable questions.
Anthology ID:
D19-3030
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
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
175–180
Language:
URL:
https://aclanthology.org/D19-3030
DOI:
10.18653/v1/D19-3030
Bibkey:
Cite (ACL):
Vishwajeet Kumar, Sivaanandh Muneeswaran, Ganesh Ramakrishnan, and Yuan-Fang Li. 2019. ParaQG: A System for Generating Questions and Answers from Paragraphs. 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 175–180, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
ParaQG: A System for Generating Questions and Answers from Paragraphs (Kumar et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/D19-3030.pdf
Data
SQuAD