EMO - NLP at S em E val-2025 Task 11: Multi-label Emotion Detection in Multiple Languages Based on XLMCNN (2025)

Abstract: This paper describes the system implemented by the EMO-NLP team for track A of task 11 in SemEval-2025: Bridging the Gap in Text-Based Emotion Detection. The task focuses on multiple datasets covering 28 languages for multi-label emotion detection. Most of these languages are low-resource languages. To achieve this goal, we propose a multilingual multi-label emotion detection system called XLMCNN, which can perform multi-label emotion detection across multiple languages. To enable emotion detection in various languages, we utilize the pre-trained model XLM-RoberTa-large to obtain embeddings for the text in different languages. Subsequently, we apply a two-dimensional convolutional operation to the embeddings to extract text features, thereby enhancing the accuracy of multi-label emotion detection. Additionally, we assign weights to different emotion labels to mitigate the impact of uneven label distribution. In this task, we focus on nine languages, among which the Amharic language achieves the best performance with our system, ranking 21st out of 45 teams.

## introduction semeval-2025 task 11 1 consists of three subtasks, each focusing on different aspects. we focus only on track a. track a aims to conduct multi-label emotion detection in 28 languages, including amharic, hausa, german, english, oromo, afrikaans, algerian arabic, chinese, emakhuwa, hindi, javanese, kinyarwanda, marathi, moroccan arabic, nigerian-pidgin, portuguese(brazilian), portuguese(mozambican), romanian, russian, somali, spanish(latin american), sundanese, swahili, swedish, tatar, tigrinya, ukrainian, yoruba, identifying the emotion labels contained in a given sentence of a specific language @xcite . every sentence 1 https://github.com/emotion-analysis-project/ semeval2025-task11 in datasets may contain zero, one, or multiple emotions. emotion detection is one of the important research directions in the field of natural language processing. many emotion detection applications exist, such as recommendation systems @xcite and public opinion monitoring @xcite . however, the majority of research on emotion detection has focused on high-resource languages, with relatively little attention given to low-resource languages. the brighter dataset as well as datasets of amharic, oromo, somali, and tigrinya languages @xcite collected for semeval-2025 task 11 includes low-resource languages from africa, asia, latin america, and other regions, providing data support for multi-label emotion detection in low-resource languages. we focus on the multi-label emotion detection of nine languages in track a, including amharic, german, english, oromo, russian, portuguese (brazilian), sundanese, somali, and tigrinya. for these nine languages, we propose the xlmcnn, a multilingual multi-label emotion detection system, which can directly process preprocessed sentence texts from different languages and detect their sentiment categories. we employ the pre-trained model xlm-roberta-large to obtain the vector representations of sentences. subsequently, we utilize a convolutional neural network (cnn) for feature extraction to enhance the accuracy of sentiment detection. moreover, when calculating the loss, we assign different weights to different emotion labels to mitigate the adverse effects caused by the imbalance of emotion label count. approaches to mainstream approaches, machine learning, and deep learning methods @xcite . the granularity levels of sentiment analysis research include aspects @xcite , documents @xcite , multimodal @xcite , and so on. research on sentiment polarity has achieved significant success in high-resource languages. however, for multi-label emotion detection, especially in low-resource languages, methods suitable for single-label emotion detection are not applicable due to the co-occurrence of emotion labels @xcite . so, more and more researchers have begun to explore new methods for multi-label text emotion detection. in 2014, jabreel and moreno @xcite proposed a method combining attention models with bidirectional gated recurrent units (bi-gru) to identify the associations between emotion label and the words in a sentence, thereby achieving multilabel sentiment classification. in 2021, alhuzali and ananiadou (alhuzali and ananiadou, 2021) used a bert encoder to make emotion labels and the entire sentence as input to capture the associations between emotion label and all words in the sentence. in 2023, ameer et al. @xcite implemented multi-label emotion detection using roberta and multi-layer attention mechanisms. in 2023, zahra ahanin et al. @xcite used a combination of deep learning-based features and human-engineered features to improve the accuracy of multi-label text classification. ## system overview in this section, we provide an overview of the system implementation process and offer an introduction to the details of the system. ## experiment in this section, we introduce the dataset of semeval-2025 task 11 and how we preprocess these data. additionally, we also show our experiment configurations. ## result and analysis in this section, we present the test set results that are ultimately submitted to the system, comparing the results between the cases of assigning weights to the labels and not assigning weights. ## conclusion in this paper, we employ a method that combines the xlm-roberta-large model with a convolutional neural network while weighting emotion labels to achieve multi-label text emotion detection in multiple languages. the final experimental results indicate that xlmcnn has better overall performance in handling multi-label emotion detection than the method without label weighting. the method we use generally performs across languages, especially sundanese, oromo, and tigrinya, with a very poor prediction for **fear** emotion. in future work, we will explore the issues within the proposed method and improve the performance of xlmcnn in low-resource languages.