arXiv 2505.132442025-05-19cs.CLcs.LG

JNLP 在 SemEval-2025 任务11中的表现：基于生成式模型的跨语言多标签情感检测

JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models

Japan Advanced Institute of Science and Technology（日本先进科学研究所）；ROIS-DS Center for Juris-Informatics, NII, Tokyo, Japan（日本信息机构ROIS-DS法律信息中心）；National Institute of Advanced Industrial Science and Technology（国家先进工业科学和技术研究所）

Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen, Xin Liu

备注：: Published in The 19th International Workshop on Semantic Evaluation (SemEval-2025)

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AI总结：

本研究针对SemEval-2025任务11的多语言情感检测两个子赛道，结合预训练多语言模型与两种分类架构，提出基础法和成对法处理多标签分类，在多语言赛道取得优异排名，验证了方法的泛化性与有效性。

摘要：

随着全球数字化的快速推进，来自不同国家的用户愈发依赖社交媒体进行信息交流。在此背景下，多语言多标签情感检测已成为一个关键研究领域。本研究针对SemEval-2025任务11：弥补文本情感检测中的差距展开。我们的论文聚焦于该任务的两个子赛道：（1）赛道A：多标签情感检测，以及（2）赛道B：情感强度。为应对多语言挑战，我们利用预训练多语言模型，并聚焦两种架构：（1）基于微调BERT的分类模型，以及（2）经指令微调的生成式大语言模型（LLM）。此外，我们提出了两种处理多标签分类的方法：基础方法，直接将输入映射到其所有对应的情感标签；以及成对方法，分别对输入文本与每个情感类别之间的关系建模。实验结果证明了我们的方法在多语言情感识别中具备强泛化能力。在赛道A中，我们的方法在10种语言上取得了前4的成绩，其中印地语排名第1。在赛道B中，我们的方法也在7种语言中获得了前5的表现，凸显了其简洁性与有效性。我们的代码可在https://github.com/yingjie7/mlingual_multilabel_emo_detection获取。

英文摘要：

With the rapid advancement of global digitalization, users from different countries increasingly rely on social media for information exchange. In this context, multilingual multi-label emotion detection has emerged as a critical research area. This study addresses SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. Our paper focuses on two sub-tracks of this task: (1) Track A: Multi-label emotion detection, and (2) Track B: Emotion intensity. To tackle multilingual challenges, we leverage pre-trained multilingual models and focus on two architectures: (1) a fine-tuned BERT-based classification model and (2) an instruction-tuned generative LLM. Additionally, we propose two methods for handling multi-label classification: the base method, which maps an input directly to all its corresponding emotion labels, and the pairwise method, which models the relationship between the input text and each emotion category individually. Experimental results demonstrate the strong generalization ability of our approach in multilingual emotion recognition. In Track A, our method achieved Top 4 performance across 10 languages, ranking 1st in Hindi. In Track B, our approach also secured Top 5 performance in 7 languages, highlighting its simplicity and effectiveness\footnote{Our code is available at https://github.com/yingjie7/mlingual_multilabel_emo_detection.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。