Learning from Auxiliary Sources in Argumentative Revision Classification
Tazin Afrin Diane Litman
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.