Diversity Aware Relevance Learning for Argument Search

In this work, we focus on 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 cannot utilize extensive 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 explicitly identifying duplicates. Our empirical evaluation demonstrates that our approach leads to a significant improvement in the argument retrieval task, even though it requires fewer data than prior methods. Our code is available at https://github.com/fromm-m/ecir2021-am-search.