@inproceedings{yu-liu-2026-kevinyu66,
title = "kevinyu66 at {S}em{E}val-2026 Task 3: A Retrieval-Augmented {LLM} System for Aspect{--}Opinion Triplet Extraction",
author = "Yu, Kuanlin and
Liu, Wen-Ni",
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-1.16/",
pages = "108--114",
ISBN = "979-8-89176-414-9",
abstract = "This paper describes our system used in the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. To address the inherent subjectivity and nuanced emotional expressions in this task, we propose a Retrieval-Augmented Generation (RAG) framework based on Large Language Models (LLMs) for sentiment triplet extraction. Our approach leverages a dynamic retrieval mechanism to identify semantically similar training examples, which are then integrated into the prompts as in-context demonstrations. This strategy effectively guides the model{'}s inference process by providing relevant linguistic patterns and emotional contexts. Our implementation is available at https://github.com/Kevinyu66/dimaste."
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<abstract>This paper describes our system used in the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. To address the inherent subjectivity and nuanced emotional expressions in this task, we propose a Retrieval-Augmented Generation (RAG) framework based on Large Language Models (LLMs) for sentiment triplet extraction. Our approach leverages a dynamic retrieval mechanism to identify semantically similar training examples, which are then integrated into the prompts as in-context demonstrations. This strategy effectively guides the model’s inference process by providing relevant linguistic patterns and emotional contexts. Our implementation is available at https://github.com/Kevinyu66/dimaste.</abstract>
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%0 Conference Proceedings
%T kevinyu66 at SemEval-2026 Task 3: A Retrieval-Augmented LLM System for Aspect–Opinion Triplet Extraction
%A Yu, Kuanlin
%A Liu, Wen-Ni
%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 yu-liu-2026-kevinyu66
%X This paper describes our system used in the SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis. To address the inherent subjectivity and nuanced emotional expressions in this task, we propose a Retrieval-Augmented Generation (RAG) framework based on Large Language Models (LLMs) for sentiment triplet extraction. Our approach leverages a dynamic retrieval mechanism to identify semantically similar training examples, which are then integrated into the prompts as in-context demonstrations. This strategy effectively guides the model’s inference process by providing relevant linguistic patterns and emotional contexts. Our implementation is available at https://github.com/Kevinyu66/dimaste.
%U https://aclanthology.org/2026.semeval-1.16/
%P 108-114
Markdown (Informal)
[kevinyu66 at SemEval-2026 Task 3: A Retrieval-Augmented LLM System for Aspect–Opinion Triplet Extraction](https://aclanthology.org/2026.semeval-1.16/) (Yu & Liu, SemEval 2026)
ACL