Automated Essay scoring has been explored as a research and industry problem for over 50 years. It has drawn a lot of attention from the NLP community because of its clear educational value as a research area that can engender the creation of valuable time-saving tools for educators around the world. Yet, these tools are generally focused on detecting good grammar, spelling mistakes, and organization quality but tend to fail at incorporating persuasiveness features in their final assessment. The responsibility to give actionable feedback to the student to improve the strength of their arguments is left solely on the teacher's shoulders. In this work, we present a transformer-based architecture capable of achieving above-human accuracy in annotating argumentative writing discourse elements for their persuasiveness quality and we expand on planned future work investigating the explainability of our model so that actionable feedback can be offered to the student and thus potentially enable a partnership between the teacher's advice and the machine's advice.

翻译：自动作文评分作为研究和工业问题已被探索超过50年。由于其在教育领域的明确价值——能够为全球教育工作者创造宝贵的省时工具，这一研究领域引起了自然语言处理社区的广泛关注。然而，这些工具通常侧重于检测语法正确性、拼写错误和文章组织质量，但在最终评估中往往未能融入说服力特征。向学生提供可操作的反馈以增强其论证力度的责任完全落在教师肩上。在本工作中，我们提出了一种基于Transformer的架构，能够在标注议论文写作话语元素的说服力质量方面达到超越人类的准确率，并扩展了未来工作计划，研究我们模型的可解释性，以便向学生提供可操作的反馈，从而有望实现教师建议与机器建议之间的协作。