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Many annotation tasks in natural language processing are highly subjective inthat there can be different valid and justified perspectives on what is aproper label for a given example. This also applies to the judgment of argumentquality, where the assignment of a single ground truth is often questionable.At the same time, there are generally accepted concepts behind argumentationthat form a common ground. To best represent the interplay of individual andshared perspectives, we consider a continuum of approaches ranging from modelsthat fully aggregate perspectives into a majority label to "sharenothing"-architectures in which each annotator is considered in isolation fromall other annotators. In between these extremes, inspired by models used in thefield of recommender systems, we investigate the extent to which architecturesthat include layers to model the relations between different annotators arebeneficial for predicting single-annotator labels. By means of two tasks ofargument quality classification (argument concreteness and validity/novelty ofconclusions), we show that recommender architectures increase the averagedannotator-individual F$_1$-scores up to $43\%$ over a majority label model. Ourfindings indicate that approaches to subjectivity can benefit from relatingindividual perspectives.

Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It's Best to Relate Perspectives!