Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning
Semere Kiros Bitew, Johannes Deleu, A. Seza Doğruöz, Chris Develder, Thomas Demeester
Abstract
Since performing exercises (including, e.g.,practice tests) forms a crucial component oflearning, and creating such exercises requiresnon-trivial effort from the teacher. There is agreat value in automatic exercise generationin digital tools in education. In this paper, weparticularly focus on automatic creation of gap-filling exercises for language learning, specifi-cally grammar exercises. Since providing anyannotation in this domain requires human ex-pert effort, we aim to avoid it entirely and ex-plore the task of converting existing texts intonew gap-filling exercises, purely based on anexample exercise, without explicit instructionor detailed annotation of the intended gram-mar topics. We contribute (i) a novel neuralnetwork architecture specifically designed foraforementioned gap-filling exercise generationtask, and (ii) a real-world benchmark datasetfor French grammar. We show that our modelfor this French grammar gap-filling exercisegeneration outperforms a competitive baselineclassifier by 8% in F1 percentage points, achiev-ing an average F1 score of 82%. Our model im-plementation and the dataset are made publiclyavailable to foster future research, thus offeringa standardized evaluation and baseline solutionof the proposed partially annotated data predic-tion task in grammar exercise creation.- Anthology ID:
- 2023.bea-1.51
- Volume:
- Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, Torsten Zesch
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 598–609
- Language:
- URL:
- https://aclanthology.org/2023.bea-1.51
- DOI:
- 10.18653/v1/2023.bea-1.51
- Cite (ACL):
- Semere Kiros Bitew, Johannes Deleu, A. Seza Doğruöz, Chris Develder, and Thomas Demeester. 2023. Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning. In Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), pages 598–609, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning (Bitew et al., BEA 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.bea-1.51.pdf