Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval

TL;DR

This paper introduces PerspectiveArg2024, a shared task on perspective argument retrieval that emphasizes incorporating socio-cultural perspectives and biases in multilingual argument retrieval systems.

Contribution

It presents a novel multilingual dataset with socio-cultural variables and explores how retrieval systems consider explicit and implicit perspectives, highlighting challenges and biases.

Findings

Systems struggle with implicit perspectives and personalization.

Retrieval tends to favor majority groups but reduces bias for females.

Significant challenges remain in reducing polarization and bias.

Abstract

Argument retrieval is the task of finding relevant arguments for a given query. While existing approaches rely solely on the semantic alignment of queries and arguments, this first shared task on perspective argument retrieval incorporates perspectives during retrieval, accounting for latent influences in argumentation. We present a novel multilingual dataset covering demographic and socio-cultural (socio) variables, such as age, gender, and political attitude, representing minority and majority groups in society. We distinguish between three scenarios to explore how retrieval systems consider explicitly (in both query and corpus) and implicitly (only in query) formulated perspectives. This paper provides an overview of this shared task and summarizes the results of the six submitted systems. We find substantial challenges in incorporating perspectivism, especially when aiming for personalization based solely on the text of arguments without explicitly providing socio profiles. Moreover, retrieval systems tend to be biased towards the majority group but partially mitigate bias for the female gender. While we bootstrap perspective argument retrieval, further research is essential to optimize retrieval systems to facilitate personalization and reduce polarization.