@inproceedings{m-etal-2026-cryptix,
title = "Cryptix at {S}em{E}val-2026 Task 4: Zero-Shot Bi-Encoder Modeling for Narrative Story Similarity - A Sentence Transformer Approach",
author = "M, Sushmitha and
P, Sarath Kumar and
S, Thanalaxmi and
A, Beulah",
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-1.168/",
pages = "1282--1287",
ISBN = "979-8-89176-414-9",
abstract = "This submission presents a zero-shot embedding-based approach for SemEval-2026 Task 4 on Narrative Story Similarity. The system employs the pretrained sentence-transformers/all-mpnet-base-v2 model within a bi-encoder architecture to generate 768-dimensional story embeddings. Narrative similarity is modeled using cosine similarity in embedding space for comparative prediction in Track A and representation generation in Track B. The approach does not involve task-specific fine-tuning and treats narrative comparison as a geometric proximity problem. Experimental results and error analysis highlight the strengths of pretrained semantic encoders in capturing thematic similarity, while revealing limitations in modeling deeper narrative structure and causal progression."
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<abstract>This submission presents a zero-shot embedding-based approach for SemEval-2026 Task 4 on Narrative Story Similarity. The system employs the pretrained sentence-transformers/all-mpnet-base-v2 model within a bi-encoder architecture to generate 768-dimensional story embeddings. Narrative similarity is modeled using cosine similarity in embedding space for comparative prediction in Track A and representation generation in Track B. The approach does not involve task-specific fine-tuning and treats narrative comparison as a geometric proximity problem. Experimental results and error analysis highlight the strengths of pretrained semantic encoders in capturing thematic similarity, while revealing limitations in modeling deeper narrative structure and causal progression.</abstract>
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%0 Conference Proceedings
%T Cryptix at SemEval-2026 Task 4: Zero-Shot Bi-Encoder Modeling for Narrative Story Similarity - A Sentence Transformer Approach
%A M, Sushmitha
%A P, Sarath Kumar
%A S, Thanalaxmi
%A A, Beulah
%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 m-etal-2026-cryptix
%X This submission presents a zero-shot embedding-based approach for SemEval-2026 Task 4 on Narrative Story Similarity. The system employs the pretrained sentence-transformers/all-mpnet-base-v2 model within a bi-encoder architecture to generate 768-dimensional story embeddings. Narrative similarity is modeled using cosine similarity in embedding space for comparative prediction in Track A and representation generation in Track B. The approach does not involve task-specific fine-tuning and treats narrative comparison as a geometric proximity problem. Experimental results and error analysis highlight the strengths of pretrained semantic encoders in capturing thematic similarity, while revealing limitations in modeling deeper narrative structure and causal progression.
%U https://aclanthology.org/2026.semeval-1.168/
%P 1282-1287
Markdown (Informal)
[Cryptix at SemEval-2026 Task 4: Zero-Shot Bi-Encoder Modeling for Narrative Story Similarity - A Sentence Transformer Approach](https://aclanthology.org/2026.semeval-1.168/) (M et al., SemEval 2026)
ACL