通过SBERT嵌入和LSTM-Attention网络进行自动化作文评分
1School of Foreign Languages, Shanghai University, Shanghai, China.
PeerJ. Computer science
|March 10, 2025
概括
本研究介绍了一种先进的自动化作文评分 (AES) 系统,使用Sentence-BERT和LSTM与注意力. 这种创新方法显著提高了得分准确度,改善了教育技术评估.
科学领域:
- 教育技术的教育技术
- 自然语言处理自然语言处理.
- 教育中的人工智能
背景情况:
- 自动化作文评分 (AES) 对于高效和客观的学生写作评估至关重要.
- 现有的AES方法在捕捉细微的语言特征和上下文含义方面面临挑战.
- 需要更复杂的模型来提高自动化评估的准确性和可靠性.
研究的目的:
- 开发和评估一种创新的AES方法.
- 通过整合先进的深度学习技术来提高评分的准确性.
- 提高自动化写作评估系统的可靠性和效率.
主要方法:
- 整合句子-BERT (SBERT) 来生成文章嵌入.
- 使用双向长短期内存 (BiLSTM) 网络来处理顺序嵌入数据.
- 整合注意力机制,专注于突出文章组件.
主要成果:
- 拟议的SBERT-BiLSTM与注意力模型在得分准确度方面取得了显著的改进.
- 该系统有效地学习并利用了来自 essay embedding vectors 的特征.
- 注意力机制增强了模型优先考虑关键文本元素的能力.
结论:
- 开发的AES方法为自动化写作评估提供了实质性的进步.
- 这种方法为传统的评分技术提供了更可靠,更有效的替代方案.
- 这些发现强调了结合SBERT,BiLSTM和关注教育技术应用的潜力.
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