@inproceedings{istrate-etal-2026-narrative,
title = "Narrative Team at {S}em{E}val-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Sentences through Narrative Understanding",
author = "Istrate, Valentin and
Octavian, Mocanu and
Khaidukova, Tatiana",
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://aclanthology.org/2026.semeval-1.148/",
pages = "1089--1093",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes our system for SemEval-2026 Task 5, which focuses on predicting the plausibility of word senses in ambiguous narrative contexts. The task requires assigning a real-valued plausibility score to candidate word senses based on aggregated human judgments. Our approach compares two modeling paradigms: (i) a pretrained transformer-based regression model using DistilBERT fine-tuned on the task data, and (ii) a lightweight neural baseline based on a bidirectional LSTM trained either from scratch or initialized with GloVe embeddings. Input representations combine a candidate sense definition with the narrative context and target sentence, separated by a special token. On the official test set, the DistilBERT model achieves the strongest result among our submissions, with an Acc@SD score of 0.54 and Spearman correlation of 0.17, while the best BiLSTM submission reaches 0.52 Acc@SD and 0.02 Spearman correlation. Although DistilBERT performs best in our experiments, the recurrent baseline remains competitive under the tolerance-based metric. We discuss model variants, reproducibility details, and limitations of our analysis."
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<abstract>This paper describes our system for SemEval-2026 Task 5, which focuses on predicting the plausibility of word senses in ambiguous narrative contexts. The task requires assigning a real-valued plausibility score to candidate word senses based on aggregated human judgments. Our approach compares two modeling paradigms: (i) a pretrained transformer-based regression model using DistilBERT fine-tuned on the task data, and (ii) a lightweight neural baseline based on a bidirectional LSTM trained either from scratch or initialized with GloVe embeddings. Input representations combine a candidate sense definition with the narrative context and target sentence, separated by a special token. On the official test set, the DistilBERT model achieves the strongest result among our submissions, with an Acc@SD score of 0.54 and Spearman correlation of 0.17, while the best BiLSTM submission reaches 0.52 Acc@SD and 0.02 Spearman correlation. Although DistilBERT performs best in our experiments, the recurrent baseline remains competitive under the tolerance-based metric. We discuss model variants, reproducibility details, and limitations of our analysis.</abstract>
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%0 Conference Proceedings
%T Narrative Team at SemEval-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Sentences through Narrative Understanding
%A Istrate, Valentin
%A Octavian, Mocanu
%A Khaidukova, Tatiana
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F istrate-etal-2026-narrative
%X This paper describes our system for SemEval-2026 Task 5, which focuses on predicting the plausibility of word senses in ambiguous narrative contexts. The task requires assigning a real-valued plausibility score to candidate word senses based on aggregated human judgments. Our approach compares two modeling paradigms: (i) a pretrained transformer-based regression model using DistilBERT fine-tuned on the task data, and (ii) a lightweight neural baseline based on a bidirectional LSTM trained either from scratch or initialized with GloVe embeddings. Input representations combine a candidate sense definition with the narrative context and target sentence, separated by a special token. On the official test set, the DistilBERT model achieves the strongest result among our submissions, with an Acc@SD score of 0.54 and Spearman correlation of 0.17, while the best BiLSTM submission reaches 0.52 Acc@SD and 0.02 Spearman correlation. Although DistilBERT performs best in our experiments, the recurrent baseline remains competitive under the tolerance-based metric. We discuss model variants, reproducibility details, and limitations of our analysis.
%U https://aclanthology.org/2026.semeval-1.148/
%P 1089-1093
Markdown (Informal)
[Narrative Team at SemEval-2026 Task 5: Rating Plausibility of Word Senses in Ambiguous Sentences through Narrative Understanding](https://aclanthology.org/2026.semeval-1.148/) (Istrate et al., SemEval 2026)
ACL