Tue- JMS at S em E val-2025 Task 11: KR e L ax: An Ensemble-Based Approach for Multilingual Emotion Detection and Addressing Data Imbalance (2025)

Abstract: Emotion detection research has primarily focused on English, leaving a gap for low-resource languages. To address this, we present KReLaX, a multilingual ensemble model for multi-label emotion detection, combining three BERT-based encoders with a weighted voting layer. Within the shared task, our system performed well in multi-label classification, ranking 3rd in Tatar and achieving strong results in Hindi, Russian, Marathi, and Spanish. In emotion intensity classification, we achieved 6th place in Amharic and Hausa. While our system struggled in the zero-shot track, it achieved 7th place in Indonesian. These results highlight both the potential and the challenges of multilingual emotion detection, emphasizing the need for improved generalization in low-resource settings.

## introduction emotion detection goes beyond traditional sentiment analysis by interpreting the emotional tone, mood, or psychological state conveyed in an utterance. emotions are multi-dimensional, contextdependent, and differ across cultures and individuals @xcite , making their interpretation in language challenging. semeval-2025 task 11 @xcite focuses on perceived emotionsdetermining which emotions most people would attribute to a speaker given a short text. while significant progress has been made in emotion detection in high-resource languages, particularly english @xcite , there remains a gap in resources and systems for low-resource languages. to address this, the task encourages the development of multi-label emotion detection systems for underrepresented languages, spanning three tracks: in this paper, we present our multilingual ensemble system, krelax, foot_0 developed for all three tracks. our approach leverages cross-lingual transfer learning @xcite to improve emotion detection in low-resource languages and applies data augmentation to improve robustness @xcite . our system is a transformer-based ensemble model that combines multiple multilingual bert variants with a weighted prediction layer. prior work has shown that ensemble architectures can help mitigate bias and improve generalization in text classification tasks @xcite . we evaluate our system across all tracks, analyzing the impact of multilinguality, data augmentation, and ensemble learning on performance. our results show strong performance in multi-label classification (ranking 3rd in tatar) and emotion intensity classification (6th in amharic and hausa). however, zero-shot performance in track c was challenging, highlighting the need for improved cross-lingual generalization. ## task description the focus of the task is on perceived emotions: determining which emotions most people would associate with a speaker based on a sentence or short text. the brighter collection of emotion recognition datasets was created for the purposes of the task; it contains datasets for 28 languages, including many low-resource languages @xcite . four additional languages were drawn from the ethioemo dataset @xcite . a breakdown of the provided languages and datasets is shown in the training data for the task consists of small texts from various sources in 28 languages for track a, and 11 languages for track b; as track c concerns cross-lingual emotion detection, no training data is provided for this track. each text is annotated as follows, depending on the task: detection): each emotion -anger, disgust, fear, joy, sadness, and surprise -is labeled as either present ("1") or absent ("0"). each text can have multiple emotion labels, resulting in label imbalance where certain emotions are underrepresented. to address this, we assume independence between emotion labels and approach the task as a series of binary classification problems. ## system overview our system implements an ensemble learning approach, where multiple models make predictions, and final predictions are determined via a weighted soft voting layer to determine the final classification. the model architecture diagram is shown in figure 1 . we fine-tuned three multilingual models in the bert family -xlm-roberta @xcite , labse @xcite , and rembert (chung et al., 2021) on sequence classification. each model was trained on all languages included in track a's training data, excluding afrikaans due to the difficulty in handling the label alignment (the afrikaans dataset only included 5 out of 6 emotions -the same was true for english, but we opted to still use the english data due to it being one of the larger datasets provided). ## experimental setup our system was fine-tuned on the combined training and development data for track a, excluding afrikaans. samples from the augmented data were randomly selected to be included in the training and validation data to balance the classes via stratified sampling, while keeping the proportion of augmented data below 20%. more information on our data augmentation techniques are included in the section below. the training and development datasets were combined and split into 3 proportional folds for cross validation. we used the adamw optimizer @xcite with a learning rate of 1e-5, a batch size of 16 and a maximum sequence length of 128. overall, 19.1% of the texts provided for the task were longer than this, with minimal variance across the training, development and test splits, but with large differences between languages (from 0.2% in spanish to 57.5% in german). text length seemed to have little effect on accuracy. the model was trained over 20 epochs using 3-fold cross validation with early stopping and a weight decay of .01. we opted not to use augmented data for track b. this is because substitution and rephrasing may unintentionally affect emotional intensity, and we had no objective way to test for this effect. ## results the following sections detail our results in each track. each model was fine-tuned on the collated training data for track a, which consisted of the languages listed in ## conclusion using an ensemble method and data augmentation, we developed an emotion classification system that performs well across multiple languages. our system achieved strong results in track a, particularly in hindi, russian, marathi, spanish, and tatar, and showed competitive performance in track b for amharic, hausa, and russian. however, track b posed greater challenges as the data augmentation methods we used in track a were not directly applicable to emotion intensity classification. future work could explore alternative augmentation strategies to preserve intensity information. our results in track c highlight the difficulties of zero-shot classification, particularly for lowresource languages and languages with typological differences from the training data. performance on unseen languages appears to be influenced by linguistic similarity to training languages, suggesting that further cross-lingual generalization techniques could improve robustness. potential future improvements include addressing the label misalignment, experimenting with decoder-based architectures, and refining data augmentation techniques for enhancing both emotion intensity predictions and generalization to unseen languages.