Learning from Auxiliary Sources in Argumentative Revision Classification
This work addresses the challenge of enhancing revision classification in educational or writing assistance tools, though it is incremental as it builds on existing learning techniques.
The paper tackled the problem of classifying desirable reasoning revisions in argumentative writing by exploring multi-task and transfer learning to leverage auxiliary data sources, resulting in improved classifier performance over baselines as shown in intrinsic and extrinsic evaluations.
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.