UAlberta at SemEval-2026 Task 5: Disambiguating Stories via Task Decomposition

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

UAlberta's system for SemEval-2026 Task 5 addresses the challenge of disambiguating stories by predicting sense plausibility within short narratives. The core methodology involves task decomposition, where the overarching problem is broken down into more manageable subtasks, whose outputs are then combined to form a final prediction. Performance is further bolstered through the ensembling of complementary signals, including traditional word sense disambiguation (WSD) techniques and advanced fine-tuned embedding models. The research also empirically validates the "one-homonym-per-translation" principle, originally posited by Hauer and Kondrak (2020a). This robust ensemble system demonstrated competitive performance in the official evaluation, with its code and data accessible on GitHub for further research and development.

Key takeaway

For NLP engineers developing systems for narrative understanding or semantic disambiguation, you should consider implementing task decomposition. Breaking down complex problems into simpler subtasks, then combining their outputs, can significantly improve model performance. Furthermore, integrating complementary signals, such as word sense disambiguation and fine-tuned embedding models, offers a robust strategy to enhance predictive accuracy. Explore these architectural patterns to build more effective and competitive systems for sense plausibility tasks.

Key insights

Task decomposition and ensembling complementary signals significantly improve sense plausibility prediction in short narratives.

Principles

• Task decomposition simplifies complex NLP.
• Ensembling diverse signals enhances accuracy.
• "One-homonym-per-translation" principle is supported.

Method

The method involves decomposing the sense plausibility prediction into simpler subtasks, then combining their outputs. Performance is improved by ensembling word sense disambiguation and fine-tuned embedding models.

In practice

• Apply task decomposition to NLP challenges.
• Combine WSD with fine-tuned embeddings.
• Investigate homonym principles in translation.

Topics

• SemEval-2026 Task 5
• Narrative Disambiguation
• Task Decomposition
• Word Sense Disambiguation
• Embedding Models
• Ensemble Methods

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.