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Diversity Aware Relevance Learning for Argument Search

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Thomas Seidl

Prof. Dr.

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Database Systems, Data Mining and AI

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.

inproceedings

ECIR 2021

43rd European Conference on Information Retrieval. Virtual, Mar 28-Apr 01, 2021.
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Authors

M. Fromm • M. Berrendorf • S. Gilhuber • T. Seidl • E. Faerman

Links

DOI GitHub

Research Area

 A3 | Computational Models

BibTeXKey: FBO+21

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