Constrained C-Test Generation via Mixed-Integer Programming
<|reference_start|>Constrained C-Test Generation via Mixed-Integer Programming: This work proposes a novel method to generate C-Tests; a deviated form of cloze tests (a gap filling exercise) where only the last part of a word is turned into a gap. In contrast to previous works that only consider varying the gap size or gap placement to achieve locally optimal solutions, we propose a mixed-integer programming (MIP) approach. This allows us to consider gap size and placement simultaneously, achieving globally optimal solutions, and to directly integrate state-of-the-art models for gap difficulty prediction into the optimization problem. A user study with 40 participants across four C-Test generation strategies (including GPT-4) shows that our approach (MIP) significantly outperforms two of the baseline strategies (based on gap placement and GPT-4); and performs on-par with the third (based on gap size). Our analysis shows that GPT-4 still struggles to fulfill explicit constraints during generation and that MIP produces C-Tests that correlate best with the perceived difficulty. We publish our code, model, and collected data consisting of 32 English C-Tests with 20 gaps each (totaling 3,200 individual gap responses) under an open source license.<|reference_end|>
@article{lee2024constrained,
    title={Constrained C-Test Generation via Mixed-Integer Programming},
    author={Ji-Ung Lee, Marc E. Pfetsch, Iryna Gurevych},
    journal={arXiv preprint arXiv:2404.08821},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08821},
    primaryClass={cs.CL}
}