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\title{Dataset Cartography for Implicit Discourse Relation Recognition: Promises and Pitfalls}

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\author{
 \textbf{Daniil Ignatev\textsuperscript{1}},
 \textbf{Denis Paperno\textsuperscript{1}},
 \textbf{Massimo Poesio\textsuperscript{1,2}},
\\
\\
 \textsuperscript{1}Utrecht University,
 \textsuperscript{2}Queen Mary University of London,
\\
 \small{
   \textbf{Correspondence:} \href{mailto:d.ignatev@uu.nl}{d.ignatev@uu.nl}
 }
}

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\begin{document}
\maketitle
\begin{abstract}
Crowdsourced data for implicit discourse relation recognition, IDRR, has been shown to contain both plausible interpretations and noisy annotations. We present a case study of dataset cartography \cite{swayamdipta_dataset_2020} on IDRR-focused DiscoGeM corpus \cite{scholman-etal-2022-discogem}. Our findings show that error identification via low confidence proves unreliable, as confidence is strongly affected by label rarity. However, high-confidence datapoints reveal a different use case: auditing the cue-rich regions of the dataset. Our lexical probe demonstrates an association between high confidence items and (mostly temporal) intra-argument cue words. Dataset cartography can thus serve a diagnostic of cue-driven \textit{easy-to-learn} cases, which need to be balanced out to ensure the robustness of IDRR learning.
\end{abstract}

\input{IDRR_Perspectives/sections/01_Intro}
\input{IDRR_Perspectives/sections/03_Background}
\input{IDRR_Perspectives/sections/04_results}
\input{IDRR_Perspectives/sections/05_conclusion}

\section{Acknowledgments}

We thank the anonymous reviewers and Frances Yung for their helpful feedback on this work. This study is funded by NWO under the AINed Fellowship Grant NGF.1607.22.002  (``Dealing with Meaning Variation in NLP").

\section*{Limitations}

This study is limited to a particular encoder-type model architecture (DeBERTa-v3-base). Consequently, the results of our work are not guaranteed to extend to different architecture types, e.g., decoder-type models, including 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}.

In this submission, we made use of generative AI tools for assisted programming and grammar correction.




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\appendix

\include{IDRR_Perspectives/sections/09_Appendix}

\end{document}