# Learning from Auxiliary Sources in Argumentative Revision Classification

arXiv: https://arxiv.org/abs/2309.07334 · HTML: https://mlsanity.com/paper/2309.07334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 8 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [AMPERSAND: Argument Mining for PERSuAsive oNline Discussions](https://mlsanity.com/paper/2004.14677/) (2020)
- [A Corpus of Sentence-level Revisions in Academic Writing: A Step towards Understanding Statement Strength in Communication](https://mlsanity.com/paper/1405.1439/) (2014)
- [Multi-Task Learning for Argumentation Mining in Low-Resource Settings](https://mlsanity.com/paper/1804.04083/) (2018)
- [Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing](https://mlsanity.com/paper/2107.06990/) (2021)
- [An Exploratory Study of Argumentative Writing by Young Students: A Transformer-based Approach](https://mlsanity.com/paper/2006.09873/) (2020)
- [Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing](https://mlsanity.com/paper/2302.05039/) (2023)

## Abstract (arXiv)

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.