@inproceedings{roy-2026-ambirig,
    title = "Ambirig at {S}em{E}val-2026 Task 5: Distributional Ordinal Modelling for Ambiguous Word Senses in Narrative Contexts",
    author = "Roy, Soumyajit",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.84/",
    pages = "585--591",
    ISBN = "979-8-89176-414-9",
    abstract = "Word Sense Disambiguation (WSD) has traditionally been framed as selecting a single correct sense given context. However, natural language understanding by humans often involves ambiguity, underspecification, and graded plausibility judgments rather than categorical decisions. SemEval-2026 Task 5 explicitly targets this gap by requiring systems to predict human-perceived plausibility scores for word senses in short narratives. In this paper, we present a systematic empirical study of modelling plausibility as an ordinal distribution prediction problem. We hypothesise that standard classification objectives fail to capture the ordinal nature of human uncertainty in this domain. While we experimented with complex auxiliary tasks, including Siamese networks, Task-Adaptive Pretraining (TAPT), and transfer learning from Natural Language Inference (NLI), our results show these approaches fail in low-resource settings. Instead, we propose a streamlined architecture based on DeBERTa-v3-base utilising a GlossBERT-style Cross-Encoder optimised with Earth Mover{'}s Distance (EMD) loss. By modeling the problem as ordinal regression over a probability distribution and enriching inputs with prototypical examples, our system achieves an accuracy of 73{\%} and Spearman correlation of 0.593, establishing a robust baseline that outperforms complex parameter-heavy approaches."
}