Autoregressive Score Generation for Multi-trait Essay Scoring
<|reference_start|>Autoregressive Score Generation for Multi-trait Essay Scoring: Recently, encoder-only pre-trained models such as BERT have been successfully applied in automated essay scoring (AES) to predict a single overall score. However, studies have yet to explore these models in multi-trait AES, possibly due to the inefficiency of replicating BERT-based models for each trait. Breaking away from the existing sole use of encoder, we propose an autoregressive prediction of multi-trait scores (ArTS), incorporating a decoding process by leveraging the pre-trained T5. Unlike prior regression or classification methods, we redefine AES as a score-generation task, allowing a single model to predict multiple scores. During decoding, the subsequent trait prediction can benefit by conditioning on the preceding trait scores. Experimental results proved the efficacy of ArTS, showing over 5% average improvements in both prompts and traits.<|reference_end|>
@article{do2024autoregressive,
    title={Autoregressive Score Generation for Multi-trait Essay Scoring},
    author={Heejin Do, Yunsu Kim, Gary Geunbae Lee},
    journal={arXiv preprint arXiv:2403.08332},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08332},
    primaryClass={cs.CL cs.AI}
}