Towards a Holistic View on Argument Quality Prediction
This work addresses the need for a more comprehensive approach to argument quality prediction in NLP, but it is incremental as it builds on existing methods by integrating multiple angles without introducing a new paradigm.
The paper tackled the problem of automated argument quality prediction by assessing generalization across domains, interplay with related tasks, and the impact of emotions, finding that generalization requires diverse training data, argument quality is challenging but can improve other tasks, and emotions have a minor role.
Argumentation is one of society's foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing attention. A decisive property of arguments is their strength or quality. While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal. In this work, we close this gap by approaching argument quality estimation from multiple different angles: Grounded on rich results from thorough empirical evaluations, we assess the generalization capabilities of argument quality estimation across diverse domains, the interplay with related argument mining tasks, and the impact of emotions on perceived argument strength. We find that generalization depends on a sufficient representation of different domains in the training part. In zero-shot transfer and multi-task experiments, we reveal that argument quality is among the more challenging tasks but can improve others. Finally, we show that emotions play a minor role in argument quality than is often assumed.