Assessing the Sufficiency of Arguments through Conclusion Generation

TL;DR

This paper investigates whether the sufficiency of an argument can be assessed by generating its conclusion from premises using large-scale language models, showing promising results that outperform previous methods.

Contribution

It introduces a novel approach to argument sufficiency assessment by modeling conclusion generation from premises with pre-trained language models, surpassing prior classification methods.

Findings

Best model achieves an F1-score of 0.885, outperforming previous state-of-the-art.

Generated conclusions are of high quality according to manual evaluation.

Impact of generated conclusions on sufficiency assessment remains limited.

Abstract

The premises of an argument give evidence or other reasons to support a conclusion. However, the amount of support required depends on the generality of a conclusion, the nature of the individual premises, and similar. An argument whose premises make its conclusion rationally worthy to be drawn is called sufficient in argument quality research. Previous work tackled sufficiency assessment as a standard text classification problem, not modeling the inherent relation of premises and conclusion. In this paper, we hypothesize that the conclusion of a sufficient argument can be generated from its premises. To study this hypothesis, we explore the potential of assessing sufficiency based on the output of large-scale pre-trained language models. Our best model variant achieves an F1-score of .885, outperforming the previous state-of-the-art and being on par with human experts. While manual evaluation reveals the quality of the generated conclusions, their impact remains low ultimately.