Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Diversity Aware Relevance Learning for Argument Search   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.

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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: eess