Disambiguation of Emotion Annotations by Contextualizing Events in Plausible Narratives

Johannes Schaefer, Roman Klinger


Abstract
Ambiguity in emotion analysis stems both from potentially missing information and the subjectivity of interpreting a text. The latter did receive substantial attention, but can we fill missing information to resolve ambiguity? We address this question by developing a method to automatically generate reasonable contexts for an otherwise ambiguous classification instance. These generated contexts may act as illustrations of potential interpretations by different readers, as they can fill missing information with their individual world knowledge. This task to generate plausible narratives is a challenging one: We combine techniques from short story generation to achieve coherent narratives. The resulting dataset of Emotional BackStories, EBS, allows for the first comprehensive and systematic examination of contextualized emotion analysis. We conduct automatic and human annotation and find that the generated contextual narratives do indeed clarify the interpretation of specific emotions. Particularly relief and sadness benefit from our approach, while joy does not require the additional context we provide.
Anthology ID:
2026.lrec-main.757
Volume:
Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:
May
Year:
2026
Address:
Palma de Mallorca, Spain
Editors:
Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:
LREC
SIG:
Publisher:
ELRA Language Resource Association
Note:
Pages:
9635–9656
Language:
URL:
https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.757/
DOI:
Bibkey:
Cite (ACL):
Johannes Schaefer and Roman Klinger. 2026. Disambiguation of Emotion Annotations by Contextualizing Events in Plausible Narratives. International Conference on Language Resources and Evaluation, main:9635–9656.
Cite (Informal):
Disambiguation of Emotion Annotations by Contextualizing Events in Plausible Narratives (Schaefer & Klinger, LREC 2026)
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PDF:
https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.757.pdf