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.

翻译：我们开发模型以分类论证性写作中的期望推理修订。我们探索两种方法——多任务学习与迁移学习——利用类似任务的修订数据辅助源。内在与外在评估结果表明，两种方法均可提升分类器性能基准。多任务学习表明，同时在多种数据源上训练可改善性能，而迁移学习则更好地表征了数据间的关系。