YNU-HPCC at SemEval-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Stories through Narrative Understanding

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, medium

Summary

YNU-HPCC introduced its approach to SemEval 2026 Task 5, a challenge focused on evaluating the rationality of word-sense scores in ambiguous narratives. This task requires models to assess the consistency between a given word-sense definition and an ambiguous target word's meaning within a short story, then assign a rationality score on a 1-5 scale. The team experimented with several methods, including multi-head ensembles simulating individual annotators, ordinal classification and regression, and direct regression using mean squared error (MSE) or L1 loss to predict human-average consensus scores. Additionally, instructional fine-tuning with low-rank adaptation (LoRA) was explored on large language models like Qwen3-4B-Instruct and Phi-4-mini. Experimental results demonstrated that the direct MSE regression method achieved the best performance, indicating that optimizing directly for human consensus scores is highly effective for this specific task.

Key takeaway

For NLP engineers developing systems to rate semantic plausibility or narrative consistency, you should prioritize direct regression methods, specifically optimizing for mean squared error against human consensus scores. This approach proved more effective than modeling individual annotator variations or using ordinal classification for the SemEval 2026 Task 5. Consider instructional fine-tuning with LoRA on LLMs like Qwen3-4B-Instruct to enhance narrative understanding capabilities in your models.

Key insights

Directly optimizing for human consensus scores is most effective for word-sense plausibility tasks.

Principles

• Human consensus scores are a strong optimization target.
• Modeling individual annotator differences is less effective.
• Regression can outperform classification for ordered scores.

Method

The approach involved comparing multi-head ensembles, ordinal classification/regression, and direct regression (MSE/L1 loss) to predict human consensus scores, alongside LoRA fine-tuning on LLMs like Qwen3-4B-Instruct.

In practice

• Prioritize direct regression for similar scoring tasks.
• Consider LLM fine-tuning with LoRA for narrative understanding.
• Focus on aggregate human judgment, not individual variance.

Topics

• SemEval 2026 Task 5
• Word Sense Plausibility
• Narrative Understanding
• Direct Regression
• LLM Fine-tuning
• LoRA

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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