Finding Convincing Arguments Using Scalable Bayesian Preference Learning

TL;DR

This paper presents a scalable Bayesian preference learning method using Gaussian processes to identify convincing arguments from crowdsourced data, especially effective with limited or noisy data, and enhances active learning.

Contribution

It introduces a novel stochastic variational inference approach for Gaussian process preference learning to efficiently predict argument convincingness.

Findings

Outperforms previous state-of-the-art in predicting convincingness.

Effective with small, unreliable datasets.

Enhances active learning for argument evaluation.

Abstract

We introduce a scalable Bayesian preference learning method for identifying convincing arguments in the absence of gold-standard rat- ings or rankings. In contrast to previous work, we avoid the need for separate methods to perform quality control on training data, predict rankings and perform pairwise classification. Bayesian approaches are an effective solution when faced with sparse or noisy training data, but have not previously been used to identify convincing arguments. One issue is scalability, which we address by developing a stochastic variational inference method for Gaussian process (GP) preference learning. We show how our method can be applied to predict argument convincingness from crowdsourced data, outperforming the previous state-of-the-art, particularly when trained with small amounts of unreliable data. We demonstrate how the Bayesian approach enables more effective active learning, thereby reducing the amount of data required to identify convincing arguments for new users and domains. While word embeddings are principally used with neural networks, our results show that word embeddings in combination with linguistic features also benefit GPs when predicting argument convincingness.