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.

本文研究了自动论文评分中预测整体得分的多种方法，包括预训练的神经网络模型、连贯性模型和混合模型。提出了一种新颖的方法，利用基于 prompt-learning NSP 的论文连贯性特征提取和表示，通过采用句法特征密集嵌入增强 BERT-based 模型的性能，并结合连贯性、句法信息和语义嵌入进行实验，证明了优越性能。进一步分析对未来的研究也有用处。

利用神经模型中的连贯性和句法特征进行自动作文评分的潜力开发

Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring

We have developed a method for extracting the coherence features from a
paragraph by matching similar words in its sentences. We conducted an
experiment with a parallel German corpus containing 2000 human-created and 2000
machine-translated paragraphs. The result showed that our method achieved the
best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is
compared with previous methods on various computer-generated text including
translation and paper generation (best accuracy = 67.9%, equal error rate =
32.0%). Experiments on Dutch, another rich resource language, and a low
resource one (Japanese) attained similar performances. It demonstrated the
efficiency of the coherence features at distinguishing computer-translated from
human-created paragraphs on diverse languages.

通过在句子中匹配相似单词的方法，我们开发了一种从段落中提取连贯特征的方法，实验结果表明该方法在德语、荷兰语和日语等多种语言的计算机翻译文本中表现出优异的性能，对区分人工创作和机器翻译的段落具有高效性。