Automatic Argument Quality Assessment -- New Datasets and Methods

TL;DR

This paper introduces new large-scale datasets for automatic argument quality assessment and proposes neural methods based on language models, achieving competitive and superior results in argument ranking and pair classification.

Contribution

It provides the first extensive, carefully annotated datasets for argument quality and introduces neural methods that outperform previous approaches.

Findings

New datasets with 6.3k arguments and 14k pairs released

Neural methods achieve state-of-the-art in argument ranking

Significant improvement in argument-pair classification accuracy

Abstract

We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.