@inproceedings{najafi-etal-2026-marsan,
title = "{M}ar{S}an at {S}em{E}val-2026 Task 4: Narrative Similarity via Sentence-{BERT} Metric Learning with Triple-Derived Losses",
author = "Najafi, Maryam and
Tavan, Ehsan and
Colreavy, Simon",
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-1.33/",
pages = "228--234",
ISBN = "979-8-89176-414-9",
abstract = "We describe our research to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning (NSNRL). The shared task defines narrative similarity through comparative judgments over triples consisting of an anchor story and two candidates, where systems determine which candidate is narratively closer (Track A), and must output story embeddings whose cosine distances reproduce the same ordering under withheld evaluation triples (Track B). We implement a unified representation-learning approach based on a Sentence-BERT bi-encoder trained with triple-derived metric learning objectives, combining in-batch contrastive learning with explicit triplet and margin-ranking constraints. Track A is solved by direct cosine comparison between the anchor embedding and each candidate embedding, while Track B outputs normalized story vectors from the same encoder without any additional test-time modelling. During evaluation, we achieve 65.00{\%} accuracy on Track A and 65.50{\%} on Track B. These results suggest that a single, well-aligned bi-encoder can perform competitively across both tracks while remaining computationally efficient."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="najafi-etal-2026-marsan">
<titleInfo>
<title>MarSan at SemEval-2026 Task 4: Narrative Similarity via Sentence-BERT Metric Learning with Triple-Derived Losses</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maryam</namePart>
<namePart type="family">Najafi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ehsan</namePart>
<namePart type="family">Tavan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simon</namePart>
<namePart type="family">Colreavy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Kochmar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">North</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mamoru</namePart>
<namePart type="family">Komachi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-414-9</identifier>
</relatedItem>
<abstract>We describe our research to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning (NSNRL). The shared task defines narrative similarity through comparative judgments over triples consisting of an anchor story and two candidates, where systems determine which candidate is narratively closer (Track A), and must output story embeddings whose cosine distances reproduce the same ordering under withheld evaluation triples (Track B). We implement a unified representation-learning approach based on a Sentence-BERT bi-encoder trained with triple-derived metric learning objectives, combining in-batch contrastive learning with explicit triplet and margin-ranking constraints. Track A is solved by direct cosine comparison between the anchor embedding and each candidate embedding, while Track B outputs normalized story vectors from the same encoder without any additional test-time modelling. During evaluation, we achieve 65.00% accuracy on Track A and 65.50% on Track B. These results suggest that a single, well-aligned bi-encoder can perform competitively across both tracks while remaining computationally efficient.</abstract>
<identifier type="citekey">najafi-etal-2026-marsan</identifier>
<location>
<url>https://aclanthology.org/2026.semeval-1.33/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>228</start>
<end>234</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T MarSan at SemEval-2026 Task 4: Narrative Similarity via Sentence-BERT Metric Learning with Triple-Derived Losses
%A Najafi, Maryam
%A Tavan, Ehsan
%A Colreavy, Simon
%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 najafi-etal-2026-marsan
%X We describe our research to SemEval-2026 Task 4 on Narrative Story Similarity and Narrative Representation Learning (NSNRL). The shared task defines narrative similarity through comparative judgments over triples consisting of an anchor story and two candidates, where systems determine which candidate is narratively closer (Track A), and must output story embeddings whose cosine distances reproduce the same ordering under withheld evaluation triples (Track B). We implement a unified representation-learning approach based on a Sentence-BERT bi-encoder trained with triple-derived metric learning objectives, combining in-batch contrastive learning with explicit triplet and margin-ranking constraints. Track A is solved by direct cosine comparison between the anchor embedding and each candidate embedding, while Track B outputs normalized story vectors from the same encoder without any additional test-time modelling. During evaluation, we achieve 65.00% accuracy on Track A and 65.50% on Track B. These results suggest that a single, well-aligned bi-encoder can perform competitively across both tracks while remaining computationally efficient.
%U https://aclanthology.org/2026.semeval-1.33/
%P 228-234
Markdown (Informal)
[MarSan at SemEval-2026 Task 4: Narrative Similarity via Sentence-BERT Metric Learning with Triple-Derived Losses](https://aclanthology.org/2026.semeval-1.33/) (Najafi et al., SemEval 2026)
ACL