@inproceedings{borjigin-yang-2026-dutir,
title = "{DUTIR} at {S}em{E}val-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning",
author = "Borjigin, Tala and
Yang, Liang",
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-1.378/",
pages = "3010--3014",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents our approach for SemEval 2026 Task 4. Our method leverages a large language model fine-tuned via Low-Rank Adaptation, incorporates data cleaning, and employs a multi-prompt strategy, all trained on the official synthetic dataset. Evaluated on Track A, our system achieved an official score of 0.70, representing a reasonable performance under the given task constraints. In addition, we explore an alternative contrastive learning framework originally designed for Track B, where narrative-structure embeddings are learned and subsequently applied to Track A via similarity comparisons. Our analysis suggests that direct supervised adaptation may be more suitable for narrative reasoning tasks."
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%0 Conference Proceedings
%T DUTIR at SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning
%A Borjigin, Tala
%A Yang, Liang
%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 borjigin-yang-2026-dutir
%X This paper presents our approach for SemEval 2026 Task 4. Our method leverages a large language model fine-tuned via Low-Rank Adaptation, incorporates data cleaning, and employs a multi-prompt strategy, all trained on the official synthetic dataset. Evaluated on Track A, our system achieved an official score of 0.70, representing a reasonable performance under the given task constraints. In addition, we explore an alternative contrastive learning framework originally designed for Track B, where narrative-structure embeddings are learned and subsequently applied to Track A via similarity comparisons. Our analysis suggests that direct supervised adaptation may be more suitable for narrative reasoning tasks.
%U https://aclanthology.org/2026.semeval-1.378/
%P 3010-3014
Markdown (Informal)
[DUTIR at SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning](https://aclanthology.org/2026.semeval-1.378/) (Borjigin & Yang, SemEval 2026)
ACL