@inproceedings{de-santis-etal-2026-mudipa,
title = "{MUDIPA}: A Human-in-the-Loop {AI}-Assisted Platform for Discourse Graph Annotation in Multi-party Dialogues",
author = "De Santis, Laura and
Cimino, Gaetano and
Feldhus, Nils and
Carenini, Giuseppe and
Deufemia, Vincenzo",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.63/",
pages = "664--673",
ISBN = "979-8-89176-461-3",
abstract = "Annotating discourse structure in multi-party dialogues is a cognitively demanding task that requires specialized expertise, yet existing annotation tools are either fully manual or provide only limited assistance. We present MUDIPA, a web-based, human-in-the-loop platform for discourse graph annotation in multi-party dialogues, built around two pluggable AI backends: a suggestion engine that produces confidence-ranked arc proposals, and an LLM-based reasoning engine that generates on-demand natural-language rationales for candidate attachments and relation labels. In a counterbalanced, within-subjects study with eight annotators, we compare AI-assisted annotation with MUDIPA against manual annotation. The AI-assisted condition achieves higher inter-annotator agreement on relation labels, higher accuracy against the gold standard, and lower perceived workload, while requiring only a modest increase in annotation time. These findings indicate that the principal value of LLMs in discourse annotation lies in supporting human decision-making, improving both annotation quality and inter-annotator agreement relative to unassisted annotation."
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<abstract>Annotating discourse structure in multi-party dialogues is a cognitively demanding task that requires specialized expertise, yet existing annotation tools are either fully manual or provide only limited assistance. We present MUDIPA, a web-based, human-in-the-loop platform for discourse graph annotation in multi-party dialogues, built around two pluggable AI backends: a suggestion engine that produces confidence-ranked arc proposals, and an LLM-based reasoning engine that generates on-demand natural-language rationales for candidate attachments and relation labels. In a counterbalanced, within-subjects study with eight annotators, we compare AI-assisted annotation with MUDIPA against manual annotation. The AI-assisted condition achieves higher inter-annotator agreement on relation labels, higher accuracy against the gold standard, and lower perceived workload, while requiring only a modest increase in annotation time. These findings indicate that the principal value of LLMs in discourse annotation lies in supporting human decision-making, improving both annotation quality and inter-annotator agreement relative to unassisted annotation.</abstract>
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%0 Conference Proceedings
%T MUDIPA: A Human-in-the-Loop AI-Assisted Platform for Discourse Graph Annotation in Multi-party Dialogues
%A De Santis, Laura
%A Cimino, Gaetano
%A Feldhus, Nils
%A Carenini, Giuseppe
%A Deufemia, Vincenzo
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F de-santis-etal-2026-mudipa
%X Annotating discourse structure in multi-party dialogues is a cognitively demanding task that requires specialized expertise, yet existing annotation tools are either fully manual or provide only limited assistance. We present MUDIPA, a web-based, human-in-the-loop platform for discourse graph annotation in multi-party dialogues, built around two pluggable AI backends: a suggestion engine that produces confidence-ranked arc proposals, and an LLM-based reasoning engine that generates on-demand natural-language rationales for candidate attachments and relation labels. In a counterbalanced, within-subjects study with eight annotators, we compare AI-assisted annotation with MUDIPA against manual annotation. The AI-assisted condition achieves higher inter-annotator agreement on relation labels, higher accuracy against the gold standard, and lower perceived workload, while requiring only a modest increase in annotation time. These findings indicate that the principal value of LLMs in discourse annotation lies in supporting human decision-making, improving both annotation quality and inter-annotator agreement relative to unassisted annotation.
%U https://aclanthology.org/2026.emnlp-demos.63/
%P 664-673
Markdown (Informal)
[MUDIPA: A Human-in-the-Loop AI-Assisted Platform for Discourse Graph Annotation in Multi-party Dialogues](https://aclanthology.org/2026.emnlp-demos.63/) (De Santis et al., EMNLP 2026)
ACL