Year: 2026
Type: conference-paper
Abstract: This paper outlines the method submitted by team blue for the SemEval-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Sentences through Narrative (AmbiStory).The task requires predicting reasonable scores that match human thoughts and judgments instead of just picking a single correct sense as the output.This means that contextual reasoning with fine-grain contextual modeling is vital.In order to tackle this problem, we suggest a BERT-based cross-encoder regression model.This model encodes the entire narrative context, which includes the precontext, the ambiguous sentence, and the ending, along with candidate sense definitions and example usages.Unlike bi-encoder sentence-level methods, our model allows for token-level interaction between story cues and sense meanings.This interaction helps capture subtle narrative disambiguation signals.We conduct a systematic exploration of model architectures and training strategies, progressing from a sentencetransformer baseline to an optimised BERT cross-encoder.On the development set, our best configuration achieves a Spearman rank correlation of 0.66.On the official test set, the system achieves a Spearman correlation of 0.4866 and an Accuracy-within-Standard-Deviation of 0.6613, substantially outperforming sentence-transformer bi-encoder baselines.
Institution: Thapar Institute of Engineering & Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166833271