\section{Related work}

\paragraph{Disagreement and perspectives in annotation} 
Recent studies suggest that the concept of aggregated ground truth may be problematic when applied to subjective tasks (sentiment analysis, toxicity detection) and recommend using crowd labels directly \citep{aroyo&welty:AIMagazine15,diaz2018addressing, garten2019incorporating, akhtar2019new, pavlick2019inherent, ferracane-etal-2021-answer, larimore-etal-2021-reconsidering, plank-2022-problem}. The analysis of task-dependent factors behind disagreement in crowd labels has been another productive direction \cite{nie-etal-2020-learn,jiang-marneffe-2022-investigating,jiang-etal-2023-ecologically,sandri-etal-2023-dont,xu_beyond_2026}.


\paragraph {Annotator-specific label prediction models}
To embrace the distinct points of view on subjective tasks and model perspectives in annotation, researchers call for the preservation of diverging labels in data collection  \citep{aroyo&welty:AIMagazine15,poesio-et-al:NAACL19,basile2020s,prabhakaran2021releasing,Cabitza_Campagner_Basile_2023}.
%an approach labeled as \emph{Data Perspectivism}. Furthermore, Data Perspectivism encourages incorporating such labels into machine learning models \citep{Cabitza_Campagner_Basile_2023}, which may result in more nuanced algorithms that better capture the complexities of data.
%
Existing perspectivist models enhance transformer-based classifiers with trainable annotator-specific components, such as classification heads \cite{davani-etal-2022-dealing}, or embeddings \cite{deng-etal-2023-annotate}. These architectures enable the integration of annotator-level features, including basic embedded annotator identifiers, and also relevant sociodemographic variables \cite{mokhberian-etal-2024-capturing}. We will further explain the models used in our experiments in Section~\ref{sec:models}.

Nonetheless, human label variation does not solely stem from valid differences in perspective but can also result from annotation errors caused by misinterpretation. \citet{weber2024-varierrnli} propose an additional round of explanation annotation and subsequent analysis to tease apart annotation errors and genuine perspectivist label variations.  
%They found that few-shot prompting with LLMs can effectively identify annotation errors based on the additional justification annotations.
In this work, we conduct a manual analysis to estimate the proportion of annotation variation attributable to differences in interpretation versus annotation errors in IDRR, using third-person annotations of the cues underlying each assigned label.

%argued that NLI labels are affected by both plausible, perspective-dependent variation and by annotation errors.


\paragraph{Perspectives in IDRR} 
%The problem of disagreements in discourse structure in general has received considerable attention in the literature. 
%Within the CCR framework 
Low agreement in implicit DR annotation has been widely reported in existing work \cite{das-etal-2017-good,hoek-etal-2021-less}, also \cite{polakova2013introducing}.  Unlike in other subjective NLP tasks, variation in DR interpretation has not been linked to annotators' demographic factors. Instead, disagreement has been shown to arise from relation-specific ambiguities, such as whether the relation contains a 1st/2nd person conceptualizer %subjective propositional attitude, presence of a 1st/2nd person conceptualizer, volitional or non-volitional kind of relation 
\cite{sanders1992toward,SpoorenDegand+2010+241+266}. %propose outlined a number of factors that prompt disagreement on DRs;
%in doing so, they problematized the application of existing agreement measures to discourse annotation, calling for a discriminating, "soft" approach. 
Additional sources of disagreement include %the specification of cues \cite{hoek-etal-2021-less},
%confirmed the dependence of agreement on relation specifiedness (presence of connectives), whilst noting that underspecified connectives ("and", "but") do not stir much higher agreement compared to contexts lacking a connective.
%Within the RST framework \cite{mann1988rhetorical}, 
%\citet{das-etal-2017-good} argued for adopting a complex gold standard that licenses multiple interpretations. Later studies point to both 
textual ambiguity,
%(such as vague epistemic status of propositions) and annotator bias (e.g., their presupposed 
differences in background knowledge,
%) as the primary reasons for annotator disagreement in existing corpora; 
and ambiguities in relation definitions
%also contribute to this effect
\cite{zikanova2024text,zikanova2025gold,hewett-stede-2025-disagreements,ignatev2025annotator}. Several existing resources provide consistent multiple annotations designed to study disagreement in discourse interpretation \cite{hewett-stede-2025-disagreements,zeldes-etal-2025-erst,zikanova2025gold}. In this work, we use DiscoGeM, a dataset focused on \textbf{implicit} DRs.

%Though earlier work often considered only a single label per relation, more recent work has collected multiple labels to capture the various interpretations that are possible for DRs.
%\citet{Scholman2017CrowdsourcingDI} investigated connective insertion as a means of crowdsourcing PDTB-style implicit relations; their study asserted both the reliability of this method and the possibility of multiple interpretations. These findings were followed up by the construction of the multi-genre DiscoGeM corpus, which is used in the current study (see Section \ref{sec:dataset}).

%Datasets containing multiple labels are suitable for disagreement-aware modeling.
Recent studies have explored learning from label distributions
%(known as soft labels) has received attention in studies by 
\cite{yung-etal-2022-label,pyatkin-etal-2023-design,costa-kosseim-2024-exploring,long-etal-2024-multi}, and have consistently shown advantages over majority-label training. However, perspectivist models that explicitly predict the annotations of individual workers have not yet been applied to IDRR, which is a gap this work seeks to address.
%These showed soft label learning to be promising, although without a reference point in the form of other disagreement-aware models.


%Outside the discourse annotation field, natural language inference (NLI) offers meaningful approaches to pluralistic data. Specifically, the authors of ChaosNLI \cite{nie-etal-2020-learn} collected mass NLI labels for 4645 sentence pairs, showing that humans systematically disagree on a large portion of these. \citet{jiang-marneffe-2022-investigating} developed a taxonomy of linguistic reasons for disagreement. A follow-up study showed that the variation persists when the workers are asked to supply an explanation for their labels \cite{jiang-etal-2023-ecologically}. 
% ; out of the two, manual annotation shows more consistency. 

%A different task in itself, NLI has apparent similarities with IDRR, as it aims to establish the logical relationship between two propositions. The two tasks are affected by similar factors, such as ambiguous language or underspecification in the guidelines, which is why the methods of collecting and interpreting perspectives in NLI and IDRR may be partially translatable. 

