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\title{Analyzing IDRR with Data Maps}

\name{Author1, Author2, Author3} 

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         author1@xxx.yy, author2@zzz.edu, author3@hhh.com\\
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\abstract{Crowd-sourcing implicit discourse relations has been shown to yield a variety of plausible interpretations, but also to be prone to annotation errors. In this study, we apply the dataset cartography technique \cite{swayamdipta_dataset_2020} to implicit IDRR relations. We find that error identification based on low confidence is infeasible for IDRR datasets; however, high confidence in models coincides with strong textual heuristics and with high item-wise agreement, potentially pointing at shortcut use both by models and by crowd workers. Balancing out this factor along with label sparsity could contribute to the uniformity of IDRR classification as a task.}

\begin{document}

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\input{IDRR_Perspectives/sections/01_Intro}
\input{IDRR_Perspectives/sections/03_Background}
\input{IDRR_Perspectives/sections/04_results}
\input{IDRR_Perspectives/sections/05_analysis}
\input{IDRR_Perspectives/sections/08_Conclusion}

\section*{Limitations}

Our work focuses on a particular architecture (encoder-based classifier) as well as on a particular model (DeBERTa-v3-base). Consequently, the results of our work are not guaranteed to extend to different architecture types, e.g., decoder-only models, incl. most large language models (LLMs). Nevertheless, we argue that our results are still significant in the context of PDTB-style discourse parsing as well as discourse processing in general, as recent work on discourse relation classifiers has often relied on encoder-type architectures \cite{xiang2023survey,costa-kosseim-2025-multi,chistova2025bridging}.

\section{Bibliographical References}
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\appendix
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