  In the prompt-specific holistic score prediction task for Automatic Essay
Scoring, the general approaches include pre-trained neural model, coherence
model, and hybrid model that incorporate syntactic features with neural model.
In this paper, we propose a novel approach to extract and represent essay
coherence features with prompt-learning NSP that shows to match the
state-of-the-art AES coherence model, and achieves the best performance for
long essays. We apply syntactic feature dense embedding to augment BERT-based
model and achieve the best performance for hybrid methodology for AES. In
addition, we explore various ideas to combine coherence, syntactic information
and semantic embeddings, which no previous study has done before. Our combined
model also performs better than the SOTA available for combined model, even
though it does not outperform our syntactic enhanced neural model. We further
offer analyses that can be useful for future study.
