JNLP at S em E val-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models (2025)

Abstract: 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 language. In Track B, our approach also secured Top 5 performance in 7 languages, highlighting its simplicity and effectiveness.

## introduction with the rapid proliferation of social media, particularly in the context of global digital communication, online platforms have emerged as the primary medium for information dissemination @xcite . users from diverse linguistic backgrounds frequently express their opinions through comments, highlighting the growing need for cross-lingual sentiment detection @xcite . consequently, multilingual 1 our code is available at https://github.com/ yingjie7/mlingual_multilabel_emo_detection sentence-level sentiment analysis has become a critical task for tracking public sentiment @xcite . sentiment analysis is one of the most extensively studied applications in natural language processing @xcite . in text emotion recognition, it is common for a single sentence to express multiple emotions with varying intensities @xcite . however, developing reliable multilabel emotion analysis systems remains particularly challenging due to the scarcity of training data, especially for low-resource languages. additionally, pre-trained language models often have limited knowledge of these languages, further complicating the task. to address these challenges, this paper presents our approach for semeval-2025 task 11, "bridging the gap in multilingual multi-label emotion detection from text using large language models" @xcite . we participated in two tracks: track 1 (multi-label emotion detection) using the brighter dataset, which includes 28 languages @xcite , and track 2 (emotion intensity prediction), which covers 11 languages. in this study, to address the challenges of multilingual sentiment analysis, we leveraged pretrained models (such as roberta) and large language models (llms) to perform multi-label sentiment analysis on both high-resource languages like english and chinese, as well as low-resource languages such as african languages. we formulated multi-label emotion recognition as a text generation task. to overcome the challenge of limited training data, we utilize the capabilities of multilingual pre-trained language models to enhance both semantic understanding and the recognition of emotional tone, especially in low-resource languages. furthermore, to tackle the multi-label classification challenge, we propose two methods: the pairwise method and the base method. our findings also indicate that training the model on a combined multilingual dataset improves performance compared to training on individual language datasets. we present experiments comparing the applicability of these methods and conduct ablation studies to validate their effectiveness. our approach demonstrates strong performance in both multi-label emotion recognition and emotion intensity detection. in track a, it achieved top-four rankings in 10 languages, including first place in hindi. in track b, our method ranked within the top five for 7 languages, further highlighting its simplicity and effectiveness. moreover, our approach exhibits strong generalization across both competition sub-tasks, making it particularly beneficial for low-resource languages. however, as most plms are predominantly pretrained on english text, their effectiveness in multilingual sentiment analysis is often limited without additional fine-tuning is performed to optimize performance across diverse linguistic contexts @xcite . numerous studies have explored leveraging embeddings from llms for sentiment classification, using various low-resource datasets to assess their adaptability across languages @xcite . in our work, we leverage bert-based multilingual models to extend multi-label classification tasks, enabling knowledge transfer across languages. by integrating llms, we also present a pairwise emotional recognition method, which efficiently captures both emotional intensity and sentiment polarity within each sentence. this approach ensures that the model concentrates on one label at a time. additionally, we reformulate the multi-label classification task as a text generation problem, enhancing the model's adaptability and generalization across nlp tasks. ## system description in this work, the target task involves the perception of emotions in various languages, which aims to identify the emotion that most people would attribute to the speaker based on a given sentence or short text snippet. given a text input (x), a machine learning system needs to retrieve all the multi-label emotions (y e ) expressed in the given text (track a) and the intensity (y i ) of each class (track b). ## experimental setup dataset. to evaluate our methods, we use the original emotional data provided by the semeval task 11 organization. this dataset consists of three subsets: training, development, and test sets, spanning two competition phases: development and test. however, to ensure greater generalization, we consistently set aside 10% of the training data from each language as an internal development set. this held-out portion is used for hyper-parameter tuning, ensuring that the optimized checkpoints are selected based on this internal dev set. additionally, to handle multilingual data, we design two settings: (1) separated langs, where a separate model is trained for each language, and (2) mixed langs, where a single model is trained to learn all languages simultaneously. ## results overall, we evaluate our methods and their variants on the development set to select the best model and setting for each language (indicated in red in tables 3 , 4 ) for the final test submission. ## conclusion in this work, we present a multilingual emotion recognition system for semeval-2025 task 11, which demonstrates strong performance and remains competitive with the top-performing teams. to address multilingual challenges, we design two architectures, bert-based and llm-based, and introduce two strategies, pairwise and base, for handling the multi-label classification task. we conduct extensive experiments to analyze the effectiveness and limitations of each approach, aiming to provide valuable insights for multilingual emotion recognition research. the results validate the simplicity and effectiveness of our methods, highlighting their strong generalization ability and applicability to other tasks. limitations and potential improvements. despite the promising results achieved in our work, several limitations remain. first, the llm-based approach is heavily dependent on the knowledge and capabilities within the llms themselves, which may limit its adaptability to evolving data. furthermore, compared to bert-based methods, llm-based approach incurs higher computational costs, making it less efficient for large-scale or realtime applications. there are several directions for future improvement. one potential enhancement lies in the utilization of finer-grained information contained in the logits output. specifically, for the llm pairwise strategy, instead of relying solely on the final "yes" or "no" response, it is better to aggregate the logits corresponding to the tokens generating "yes" and compute a probability distribution via softmax. this would enable a more nuanced and probabilistic interpretation of the model's predictions, potentially improving robustness. another limitation is the incomplete handling of label imbalance. our current framework does not fully address the issue, which may cause the model to overfit to dominant emotional categories. future work could incorporate targeted data augmentation strategies, such as generating additional samples for underrepresented emotions, to mitigate this imbalance and enhance the overall performance and stability of the system.