@inproceedings{benlahbib-etal-2026-nlp-fsdm,
title = "{NLP}-{FSDM} at {S}em{E}val-2026 Task 4: Narrative Similarity via Multiple Negatives Ranking and Instruction-Based Embeddings",
author = "Benlahbib, Abdessamad and
Essalmani, Zouhir and
Boumhidi, Achraf and
Fahfouh, Anass and
Alami, Hamza",
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-1.88/",
pages = "612--616",
ISBN = "979-8-89176-414-9",
abstract = "The identification of narrative similarity is a complex NLP challenge that requires modeling deeper plot and thematic alignment rather than relying solely on lexical overlap. In this paper, we detail the participation of team NLP-FSDM in SemEval-2026 Task 4. Our approach utilizes the bge-large-en-v1.5 encoder. For Track A, we fine-tune it using Multiple Negatives Ranking Loss (MNRL), while for Track B we rely on the pretrained encoder to generate fixed narrative representations. We achieved an accuracy of 65.50{\%} in Track A and 62.50{\%} in Track B. This paper provides an extensive comparison of our results with competitive baselines and top-performing systems, analyzing the efficacy of dense encoders in low-resource narrative contexts."
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<abstract>The identification of narrative similarity is a complex NLP challenge that requires modeling deeper plot and thematic alignment rather than relying solely on lexical overlap. In this paper, we detail the participation of team NLP-FSDM in SemEval-2026 Task 4. Our approach utilizes the bge-large-en-v1.5 encoder. For Track A, we fine-tune it using Multiple Negatives Ranking Loss (MNRL), while for Track B we rely on the pretrained encoder to generate fixed narrative representations. We achieved an accuracy of 65.50% in Track A and 62.50% in Track B. This paper provides an extensive comparison of our results with competitive baselines and top-performing systems, analyzing the efficacy of dense encoders in low-resource narrative contexts.</abstract>
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%0 Conference Proceedings
%T NLP-FSDM at SemEval-2026 Task 4: Narrative Similarity via Multiple Negatives Ranking and Instruction-Based Embeddings
%A Benlahbib, Abdessamad
%A Essalmani, Zouhir
%A Boumhidi, Achraf
%A Fahfouh, Anass
%A Alami, Hamza
%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 benlahbib-etal-2026-nlp-fsdm
%X The identification of narrative similarity is a complex NLP challenge that requires modeling deeper plot and thematic alignment rather than relying solely on lexical overlap. In this paper, we detail the participation of team NLP-FSDM in SemEval-2026 Task 4. Our approach utilizes the bge-large-en-v1.5 encoder. For Track A, we fine-tune it using Multiple Negatives Ranking Loss (MNRL), while for Track B we rely on the pretrained encoder to generate fixed narrative representations. We achieved an accuracy of 65.50% in Track A and 62.50% in Track B. This paper provides an extensive comparison of our results with competitive baselines and top-performing systems, analyzing the efficacy of dense encoders in low-resource narrative contexts.
%U https://aclanthology.org/2026.semeval-1.88/
%P 612-616
Markdown (Informal)
[NLP-FSDM at SemEval-2026 Task 4: Narrative Similarity via Multiple Negatives Ranking and Instruction-Based Embeddings](https://aclanthology.org/2026.semeval-1.88/) (Benlahbib et al., SemEval 2026)
ACL