  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.
