IASBS at S em E val-2025 Task 11: Ensembling Transformers for Bridging the Gap in Text-Based Emotion Detection (2025)

Abstract: In this paper, we address the challenges of text-based emotion detection, focusing on multi-label classification, emotion intensity prediction, and cross-lingual emotion detection across various languages. We explore the use of advanced machine learning models, particularly transformers, in three tracks: emotion detection, emotion intensity prediction, and cross-lingual emotion detection. Our approach utilizes pre-trained transformer models, such as Gemini, DeBERTa, M-BERT, and M-DistilBERT, combined with techniques like majority voting and average ensemble voting (AEV) to enhance performance. We also incorporate multilingual strategies and prompt engineering to effectively handle the complexities of emotion detection across diverse linguistic and cultural contexts. Our findings demonstrate the success of ensemble methods and multilingual models in improving the accuracy and generalization of emotion detection, particularly for low-resource languages.

## introduction text-based emotion detection has become a critical task in the field of natural language processing (nlp), enabling systems to understand and respond to human emotions based on written language. the ability to accurately infer the emotions of speakers from their text offers vast potential in applications ranging from customer service automation to mental health monitoring and sentiment analysis (sa) in social media @xcite . however, despite significant advancements, this task remains challenging due to the complexity of emotional expressions across cultures, contexts, and languages. the present task @xcite , "bridging the gap in text-based emotion detection", focuses on understanding the perceived emotions of a speaker through short text snippets. unlike traditional sa, which often centers on the emotional impact on the reader or the identification of the speaker's true emotions-both of which are difficult to ascertain from a limited text-the focus here is on detecting the emotion that is most likely perceived by an average reader or listener. specifically, the task aims to identify emotions such as joy, sadness, fear, anger, surprise, and disgust from a variety of languages, including widely spoken ones like english, spanish, and chinese, as well as lesser-studied languages such as emakhuwa, igbo, and hausa. the task is organized into three distinct tracks: track a (multi-label emotion detection), track b (emotion intensity), and track c (cross-lingual emotion detection). track a requires systems to predict the presence or absence of specific emotions in text, encompassing the prediction of one or more perceived emotions in a given text snippet. track b involves determining the intensity of the identified emotions, ranging from no emotion to high degrees of emotion. track c challenges systems to generalize emotion detection across multiple languages, testing the models' ability to predict emotion labels for unseen languages based on training data. the challenge further emphasizes the importance of handling multilingual data and the complexities of translating emotional expressions across languages and traditions. with this in mind, various methodologies have been explored, including the use of state-of-the-art models such as microsoft's deberta @xcite , google's multilingual distilbert (m-distilbert) @xcite , and google's gemini @xcite . these models fine-tuned for different tracks, and employ techniques such as translation, prompt engineering, and model ensemble strategies like majority voting and aev, to enhance performance and address the unique challenges presented by the diverse language sets involved. the paper is organized into seven sections: section 2 covers the dataset, section 3 reviews related work, and section 4 presents the system architecture. section 5 describes the experimental setup, followed by section 6, which presents and analyzes the results. section 7 concludes with key findings and future research directions. this paper aims to present the methodology, evaluation strategies, and results of our approach to tackling the multifaceted problem of text-based emotion detection, while also exploring how advancements in multilingual nlp can bridge the gap in understanding human emotions across different languages. ## dataset the shared task utilizes a comprehensive and multilingual dataset designed to explore how emotions are perceived across different languages. the main objective is to determine which emotion people are most likely to associate with a speaker based on a brief text snippet. provided by the organizers of semeval 2025 -task 11 on text-based emotion detection, this dataset covers multiple languages and captures a broad range of linguistic and cultural nuances. for all languages except amharic, oromo, somali, and tigrinya, we have used the dataset referenced in @xcite . however, for any analysis involving one of these four languages, we have used the dataset from @xcite instead. this distinction ensures that the dataset remains culturally and contextually appropriate for each language. the emotions considered in this study include anger, disgust, fear, joy, sadness, and surprise. in track a, 28 languages are covered. for track b, 11 languages are included. in track c, 32 languages are available. our output for tracks a and c must be in a binary format (0 or 1), as 1 indicating the presence and 0 indicating the absence of a given emotion in the text. in track b, however, the output must be one of the integers from 0 to 3 for each emotion, representing its intensity level (0: no emotion, 1: low degree of emotion, 2: moderate degree of emotion, and 3: high degree of emotion). the dataset is been pre-processed by the task organizers and is divided into three key subsets: training, validation, and testing. ## related work sentiment analysis identifies the overall emotional tone of a text (positive, negative, or neutral), while ed focuses on identifying specific emotions. sa is broader, while ed provides more detailed and intense emotional insights. emotion detection from text, particularly perceived emotions, is a rich field of research in nlp, playing a crucial role in analyzing human expressions and understanding people's attitudes toward specific subjects. several studies have focused on detecting emotions based on textual cues, with a variety of approaches ranging from lexicon-based methods to advanced machine and deep learning techniques. however, the challenge of bridging the gap between text and the perception of emotions across different languages, cultures, and contexts remains an open issue. in this section, we review relevant work related to text-based emotion detection, emphasizing approaches related to multi-label emotion detection, emotion intensity prediction, and cross-lingual emotion detection. ## system architecture the system architecture for multilingual emotion detection uses back translation, multilingual models, transfer learning, llms to improve accuracy across languages. prompt engineering optimizes task processing, ensuring effective and adaptable ed across diverse datasets. ## experimental setup the experimental setup includes multiple stages aimed at optimizing performance across multilingual tasks, focusing on model transformation, finetuning, integration, and strategies like prompt engineering and ensemble learning. here's an overview of the steps and methods used. ## results in this section, we present the evaluation results for the three subtasks across multiple languages. the performance of each model is reported using f1-scores and pearson correlation scores, as appropriate for each subtask. additionally, we discuss the rankings and highlight key insights drawn from the results. track a evaluates the performance of various models, such as deberta, svm, and gemini-1.5, and investigates the effectiveness of the majority voting mechanism, which combines multiple strategies, across different languages. the f1-scores for each approach are reported in track b evaluates the performance of m-distilbert, gemini-1.5, and an aev strategy using pearson correlation scores. the results are summarized in track c investigates the ability of models to generalize across languages using distilbert, m-bert, and an aev strategy. although the f1scores of m-bert and distilbert in ## conclusion this study addresses the challenges of text-based emotion detection across multiple languages, focusing on multi-label classification, emotion intensity prediction, and cross-lingual detection. by finetuning advanced nlp models (gemini, deberta, m-bert, m-distillbert) and combining them with traditional methods like svm, strong results were achieved. a voting-based ensemble method enhanced model reliability. the approach demonstrated the effectiveness of multilingual models, especially for low-resource languages, ranking in the top 10 teams of the competition. however, improvements are needed in emotion intensity prediction and for low-resource languages. future work will refine model architectures, fine-tune llms, and explore new techniques like rag and cag for further enhancement.