论文标题
部分可观测时空混沌系统的无模型预测
Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers
论文作者
论文摘要
自动化的简短答案分级（ASAG）在教育中引起了人们的关注，这是将教育任务扩展到越来越多的学生的一种手段。自然语言处理和机器学习的最新进展在很大程度上影响了ASAG领域，我们调查了最近的研究进步。我们通过对最近发表的采用深度学习方法的方法进行全面分析来补充先前的调查。特别是，我们将分析重点放在从手工设计的功能到表示学习方法的过渡，这些方法从大型数据中自动学习了手头任务的代表性功能。我们将深度学习方法分析沿三个类别进行分析：单词嵌入，顺序模型和基于注意力的方法。深度学习对ASAG的影响与NLP的其他领域的影响不同，因为我们注意到，仅学识渊博的表示形式并不能促进获得最佳结果，而是表明他们以互补的方式与手工设计的功能一起工作。最好的性能确实是通过将精心设计的特征与最新模型提供的语义描述（如Transferalers Architectures）相结合的力量的方法来实现的。我们确定挑战并提供有关将来可以解决的研究方向的前景
Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine Learning has largely influenced the field of ASAG, of which we survey the recent research advancements. We complement previous surveys by providing a comprehensive analysis of recently published methods that deploy deep learning approaches. In particular, we focus our analysis on the transition from hand engineered features to representation learning approaches, which learn representative features for the task at hand automatically from large corpora of data. We structure our analysis of deep learning methods along three categories: word embeddings, sequential models, and attention-based methods. Deep learning impacted ASAG differently than other fields of NLP, as we noticed that the learned representations alone do not contribute to achieve the best results, but they rather show to work in a complementary way with hand-engineered features. The best performance are indeed achieved by methods that combine the carefully hand-engineered features with the power of the semantic descriptions provided by the latest models, like transformers architectures. We identify challenges and provide an outlook on research direction that can be addressed in the future
