@inproceedings{wang-etal-2025-teleai,
title = "{T}ele{AI} at {S}em{E}val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection with Prompt Engineering and Data Augmentation",
author = "Wang, Shiquan and
Li, Mengxiang and
Peng, Shengxiong and
Yu, Fang and
He, Zhongjiang and
Song, Shuangyong and
Li, Yongxiang",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.15/",
pages = "102--108",
ISBN = "979-8-89176-273-2",
abstract = "This paper presents the approach we employed in SemEval-2025 Task 11: ``Bridging the Gap in Text-Based Emotion Detection.'' The core objective of this shared task is emotion perception, focusing on determining the emotion the speaker is likely expressing when uttering a sentence or short text fragment, as perceived by the majority. In this task, we applied a prompt optimization strategy based on in-context learning, combined with data augmentation and ensemble voting techniques, to significantly enhance the model{'}s performance. Through these optimizations, the model demonstrated improved accuracy and stability in emotion detection. Ultimately, in both Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity Prediction), our approach achieved top-3 rankings across multiple languages, showcasing the effectiveness and cross-lingual adaptability of our method."
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<abstract>This paper presents the approach we employed in SemEval-2025 Task 11: “Bridging the Gap in Text-Based Emotion Detection.” The core objective of this shared task is emotion perception, focusing on determining the emotion the speaker is likely expressing when uttering a sentence or short text fragment, as perceived by the majority. In this task, we applied a prompt optimization strategy based on in-context learning, combined with data augmentation and ensemble voting techniques, to significantly enhance the model’s performance. Through these optimizations, the model demonstrated improved accuracy and stability in emotion detection. Ultimately, in both Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity Prediction), our approach achieved top-3 rankings across multiple languages, showcasing the effectiveness and cross-lingual adaptability of our method.</abstract>
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%0 Conference Proceedings
%T TeleAI at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection with Prompt Engineering and Data Augmentation
%A Wang, Shiquan
%A Li, Mengxiang
%A Peng, Shengxiong
%A Yu, Fang
%A He, Zhongjiang
%A Song, Shuangyong
%A Li, Yongxiang
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F wang-etal-2025-teleai
%X This paper presents the approach we employed in SemEval-2025 Task 11: “Bridging the Gap in Text-Based Emotion Detection.” The core objective of this shared task is emotion perception, focusing on determining the emotion the speaker is likely expressing when uttering a sentence or short text fragment, as perceived by the majority. In this task, we applied a prompt optimization strategy based on in-context learning, combined with data augmentation and ensemble voting techniques, to significantly enhance the model’s performance. Through these optimizations, the model demonstrated improved accuracy and stability in emotion detection. Ultimately, in both Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity Prediction), our approach achieved top-3 rankings across multiple languages, showcasing the effectiveness and cross-lingual adaptability of our method.
%U https://aclanthology.org/2025.semeval-1.15/
%P 102-108
Markdown (Informal)
[TeleAI at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection with Prompt Engineering and Data Augmentation](https://aclanthology.org/2025.semeval-1.15/) (Wang et al., SemEval 2025)
ACL