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Learning from partially annotated data : example-aware creation of gap-filling exercises for language learning

Semere Kiros Bitew, Johannes Deleu (UGent) , A. Seza Doğruöz (UGent) , Chris Develder (UGent) and Thomas Demeester (UGent)
(2023) Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023). p.598-609
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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.

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MLA
Bitew, Semere Kiros, et al. “Learning from Partially Annotated Data : Example-Aware Creation of Gap-Filling Exercises for Language Learning.” Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023), edited by Ekaterina Kochmar et al., Association for Computational Linguistics (ACL), 2023, pp. 598–609, doi:10.18653/v1/2023.bea-1.51.
APA
Bitew, S. K., Deleu, J., Doğruöz, A. S., Develder, C., & Demeester, T. (2023). Learning from partially annotated data : example-aware creation of gap-filling exercises for language learning. In E. Kochmar, J. Burstein, A. Horbach, R. Laarmann-Quante, N. Madnani, A. Tack, … T. Zesch (Eds.), Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023) (pp. 598–609). https://doi.org/10.18653/v1/2023.bea-1.51
Chicago author-date
Bitew, Semere Kiros, 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), edited by Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, and Torsten Zesch, 598–609. Toronto: Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.bea-1.51.
Chicago author-date (all authors)
Bitew, Semere Kiros, 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), ed by. Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, and Torsten Zesch, 598–609. Toronto: Association for Computational Linguistics (ACL). doi:10.18653/v1/2023.bea-1.51.
Vancouver
1.
Bitew SK, Deleu J, Doğruöz AS, Develder C, Demeester T. Learning from partially annotated data : example-aware creation of gap-filling exercises for language learning. In: Kochmar E, Burstein J, Horbach A, Laarmann-Quante R, Madnani N, Tack A, et al., editors. Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023). Toronto: Association for Computational Linguistics (ACL); 2023. p. 598–609.
IEEE
[1]
S. K. Bitew, J. Deleu, A. S. Doğruöz, C. Develder, and T. Demeester, “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), Toronto, Canada, 2023, pp. 598–609.
@inproceedings{01H750D35BT26RYNPE4N4MP139,
  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.
}},
  author       = {{Bitew, Semere Kiros and Deleu, Johannes and Doğruöz, A. Seza and Develder, Chris and Demeester, Thomas}},
  booktitle    = {{Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)}},
  editor       = {{Kochmar, Ekaterina and Burstein, Jill and Horbach, Andrea and Laarmann-Quante, Ronja and Madnani, Nitin and Tack, Anaïs and Yaneva, Victoria and Yuan, Zheng and Zesch, Torsten}},
  isbn         = {{9781959429807}},
  language     = {{eng}},
  location     = {{Toronto, Canada}},
  pages        = {{598--609}},
  publisher    = {{Association for Computational Linguistics (ACL)}},
  title        = {{Learning from partially annotated data : example-aware creation of gap-filling exercises for language learning}},
  url          = {{http://doi.org/10.18653/v1/2023.bea-1.51}},
  year         = {{2023}},
}
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