Diversity Aware Relevance Learning for Argument Search

TL;DR

This paper introduces a machine learning-based method for argument retrieval that emphasizes diverse aspect coverage without relying on manual annotations or duplicate removal, significantly improving retrieval performance.

Contribution

It presents a novel multi-step approach that captures semantic relationships and promotes diversity in argument retrieval, reducing data requirements compared to existing methods.

Findings

Significant improvement in argument retrieval accuracy

Effective coverage of diverse query aspects

Reduced need for manual annotation

Abstract

In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects. State-of-the-art methods rely on explicit mappings between claims and premises, and thus are unable to utilize large available collections of premises without laborious and costly manual annotation. Their diversity approach relies on removing duplicates via clustering which does not directly ensure that the selected premises cover all aspects. This work introduces a new multi-step approach for the argument retrieval problem. Rather than relying on ground-truth assignments, our approach employs a machine learning model to capture semantic relationships between arguments. Beyond that, it aims to cover diverse facets of the query, instead of trying to identify duplicates explicitly. Our empirical evaluation demonstrates that our approach leads to a significant improvement in the argument retrieval task even though it requires less data.