Learning from Auxiliary Sources in Argumentative Revision Classification

Tazin Afrin; Diane Litman

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arXiv · 2309.07334

Learning from Auxiliary Sources in Argumentative Revision Classification

Tazin Afrin·Diane Litman

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Abstract

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.

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Tazin Afrin, Diane Litman. 2023-09-13. Learning from Auxiliary Sources in Argumentative Revision Classification. https://arxiv.org/abs/2309.07334

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