@inproceedings{khaidukova-etal-2026-narrative,
title = "Narrative Team at {S}em{E}val-2026 Task 4: Two-Stage Contrastive Learning for Narrative Similarity Assessment",
author = "Khaidukova, Tatiana and
Ciobanu, Ana and
Gifu, Daniela and
Trandabat, Diana",
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-1.22/",
pages = "146--152",
ISBN = "979-8-89176-414-9",
abstract = "For SemEval-2026 Task 4, we introduce a unified two-stage framework based on a RoBERTa-large encoder. Stage 1 performs contrastive pre-training on synthetic triplets to learn general narrative similarity patterns. Stage 2 fine-tunes the model with a ranking-based objective tailored to Track A.The resulting encoder supports both binary similarity classification (Track A) and narrative embedding generation (Track B) without architectural changes. Our system achieves an accuracy of 0.64 on Track A and 0.69 on Track B, outperforming single-stage baselines and demonstrating that combining synthetic contrastive supervision with task-specific ranking yields stable and reusable narrative representations."
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<namePart type="given">Tatiana</namePart>
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<title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
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<abstract>For SemEval-2026 Task 4, we introduce a unified two-stage framework based on a RoBERTa-large encoder. Stage 1 performs contrastive pre-training on synthetic triplets to learn general narrative similarity patterns. Stage 2 fine-tunes the model with a ranking-based objective tailored to Track A.The resulting encoder supports both binary similarity classification (Track A) and narrative embedding generation (Track B) without architectural changes. Our system achieves an accuracy of 0.64 on Track A and 0.69 on Track B, outperforming single-stage baselines and demonstrating that combining synthetic contrastive supervision with task-specific ranking yields stable and reusable narrative representations.</abstract>
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<url>https://aclanthology.org/2026.semeval-1.22/</url>
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%0 Conference Proceedings
%T Narrative Team at SemEval-2026 Task 4: Two-Stage Contrastive Learning for Narrative Similarity Assessment
%A Khaidukova, Tatiana
%A Ciobanu, Ana
%A Gifu, Daniela
%A Trandabat, Diana
%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 khaidukova-etal-2026-narrative
%X For SemEval-2026 Task 4, we introduce a unified two-stage framework based on a RoBERTa-large encoder. Stage 1 performs contrastive pre-training on synthetic triplets to learn general narrative similarity patterns. Stage 2 fine-tunes the model with a ranking-based objective tailored to Track A.The resulting encoder supports both binary similarity classification (Track A) and narrative embedding generation (Track B) without architectural changes. Our system achieves an accuracy of 0.64 on Track A and 0.69 on Track B, outperforming single-stage baselines and demonstrating that combining synthetic contrastive supervision with task-specific ranking yields stable and reusable narrative representations.
%U https://aclanthology.org/2026.semeval-1.22/
%P 146-152
Markdown (Informal)
[Narrative Team at SemEval-2026 Task 4: Two-Stage Contrastive Learning for Narrative Similarity Assessment](https://aclanthology.org/2026.semeval-1.22/) (Khaidukova et al., SemEval 2026)
ACL