We develop models to classify desirable reasoning revisions in argumentative
writing. We explore two approaches -- multi-task learning and transfer learning
-- to take advantage of auxiliary sources of revision data for similar tasks.
Results of intrinsic and extrinsic evaluations show that both approaches can
indeed improve classifier performance over baselines. While multi-task learning
shows that training on different sources of data at the same time may improve
performance, transfer-learning better represents the relationship between the
data.

我们开发了模型来对论证写作中的理性修订进行分类。我们探索了两种方法 —— 多任务学习和迁移学习，以利用类似任务的辅助修订数据的优势。结果显示，这两种方法确实可以提高分类器的性能。虽然多任务学习表明同时在不同数据源上进行训练可以提高性能，但迁移学习更好地代表了数据之间的关系。

论证修订分类中的辅助来源学习

Learning from Auxiliary Sources in Argumentative Revision Classification

We develop models to classify desirable evidence and desirable reasoning
revisions in student argumentative writing. We explore two ways to improve
classifier performance - using the essay context of the revision, and using the
feedback students received before the revision. We perform both intrinsic and
extrinsic evaluation for each of our models and report a qualitative analysis.
Our results show that while a model using feedback information improves over a
baseline model, models utilizing context - either alone or with feedback - are
the most successful in identifying desirable revisions.

本研究通过探索使用文章修订的上下文和反馈信息两种方式来提高分类器性能，以区分学生论述写作中的有意义的证据和合理推理演绎，并进行严格评估和定性分析，研究发现通过使用上下文来辨别有意义的修订是最成功的。