小数据长序列下实现自动反馈生成，提升教学效率
Transfer Learning for Automated Feedback Generation on Small Datasets
- 采用三阶段迁移学习框架，适配小样本长文本数据
- 在小数据集上达成当前最佳效果，反馈准确但口语化
- 适用于教育科技场景，推动自动评分与反馈落地
反馈是学习过程中的关键环节，但依赖人工批改难以保证及时性与准确性。自动化反馈生成系统旨在解决这一问题。本文提出一种在极小数据集、超长序列条件下训练该系统的技术。两项特性使任务极具挑战性，但通过三阶段迁移学习流程，仍可实现领先性能，生成的反馈在质量上准确但缺乏人类语感。同时，论文讨论了自动作文评分与自动反馈系统在真实场景中的应用前景。
原文摘要 · Abstract (English)
Feedback is a very important part the learning process. However, it is challenging to make this feedback both timely and accurate when relying on human markers. This is the challenge that Automated Feedback Generation attempts to address. In this paper, a technique to train such a system on a very small dataset with very long sequences is presented. Both of these attributes make this a very challenging task, however, by using a three stage transfer learning pipeline state-of-the-art results can be achieved with qualitatively accurate but unhuman sounding results. The use of both Automated Essay Scoring and Automated Feedback Generation systems in the real world is also discussed.
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