Diversity Aware Relevance Learning for Argument Search
This addresses the need for efficient and diverse argument retrieval in domains like debate or legal analysis, though it is incremental as it builds on existing retrieval methods.
The paper tackled the problem of retrieving relevant arguments for a query claim while covering diverse aspects, introducing a multi-step approach that uses machine learning to capture semantic relationships and ensure diversity without explicit mappings or duplicate removal, resulting in significant improvement in argument retrieval with less data.
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.