Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts
Jun Araki, Dheeraj Rajagopal, Sreecharan Sankaranarayanan, Susan Holm, Yukari Yamakawa, Teruko Mitamura
Abstract
We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective. Our system is aimed at engaging language learners by generating multiple-choice questions which utilize specific inference steps over multiple sentences, namely coreference resolution and paraphrase detection. The system also generates correct answers and semantically-motivated phrase-level distractors as answer choices. Evaluation by human annotators indicates that our approach requires a larger number of inference steps, which necessitate deeper semantic understanding of texts than a traditional single-sentence approach.- Anthology ID:
- C16-1107
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1125–1136
- Language:
- URL:
- https://aclanthology.org/C16-1107
- DOI:
- Cite (ACL):
- Jun Araki, Dheeraj Rajagopal, Sreecharan Sankaranarayanan, Susan Holm, Yukari Yamakawa, and Teruko Mitamura. 2016. Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1125–1136, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts (Araki et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/C16-1107.pdf