T arbiat_ M odares_ S em E val2025_ T ask11_ M ulti L abel_ E motion_ T ransfer L earning (2025)

Abstract: The SemEval-2025 Task 11 addresses multi-label emotion detection, classifying perceived emotions in text. Our system targets Amharic, a morphologically complex, low-resource language. We fine-tune LaBSE with class-weighted loss for multi-label prediction.Our architecture consists of: (i) text tokenization via LaBSE, (ii) a fully connected layer with sigmoid activation for classification, and (iii) optimization using BCEWithLogitsLoss and AdamW. Ablation studies on class balancing and data augmentation showed that simple upsampling did not improve performance, highlighting the need for more sophisticated techniques.Our system ranked 14th out of 43 teams, achieving 0.4938 accuracy, 0.6931 micro-F1, and 0.6450 macro-F1, surpassing the task baseline (0.6383 macro-F1). Error analysis revealed that anger and disgust were well detected, while fear and surprise were frequently misclassified due to overlapping linguistic cues. Our findings underscore the challenges of multi-label emotion detection in low-resource languages. Future work could explore context-aware embeddings, improved data augmentation, and adaptive loss functions.

## introduction emotion detection is a key task in nlp with applications ranging from social media monitoring to mental health and human-computer interaction. unlike sentiment analysis, it identifies nuanced emotions such as anger, joy, sadness, fear, disgust, and surprise. semeval-2025 task 11 @xcite focuses on perceived emotions shaped by cultural and contextual cues. we tackle this challenge in amharic, a lowresource and morphologically complex language, by exploring the effectiveness of transfer learning in this setting. we fine-tune labse @xcite , a multilingual sentence embedding model, using a dropout-enhanced dense layer and bcewithlog-itsloss. our method avoids hand-crafted features, instead relying on pre-trained embeddings. our decision to focus on amharic was motivated by both strategic and linguistic considerations. as native speakers of persian-a low-resource and morphologically rich language-we were naturally drawn to amharic, which shares similar linguistic challenges and characteristics. although persian was not included in the competition's dataset, amharic was available, and we saw this as an opportunity to engage with a language that, like persian, is often underrepresented in nlp research. furthermore, we anticipated that the low-resource nature of amharic might lead many teams to overlook it, which strengthened our motivation to select it and address the gap. despite solid performance on high-confidence emotions, our model struggled with overlapping expressions and class imbalance. it ultimately ranked 14th out of 43 teams. code is available at: https://github.com/ amin-saeidi/semeval2025-task11 . ## background emotion detection has been widely explored in nlp, with recent advancements driven by deep learning and transformer-based models. unlike early lexicon-based approaches (mohammad and turney, 2013), modern methods leverage contextual embeddings from models like bert @xcite , roberta @xcite , and xlm-r @xcite . the semeval-2025 task 11 @xcite aims to advance perceived emotion detection by providing multilingual datasets and a structured evaluation framework. the task consists of three tracks. track a, multi-label emotion detection, involves assigning zero, one, or two emotion labels to a given text snippet. track b, emotion intensity estimation, focuses on predicting the intensity of a given emotion. track c, cross-lingual emotion detection, aims to transfer knowledge between languages, facilitating emotion recognition across linguistic boundaries. our work focuses on track a, where each text snippet is labeled with multiple emotions as binary values (1 for presence, 0 for absence). this multilabel classification setup poses unique challenges due to emotion co-occurrence and ambiguous expressions. the dataset includes multiple languages, but we specifically study amharic, leveraging the dataset from @xcite , which provides a benchmark for emotion detection in low-resource languages. several studies have contributed to advancing multi-label emotion detection. zhang et al. (2020 ) introduced a multi-modal framework capturing label dependencies, while firdaus et al. (2020) proposed a dataset for emotion recognition in dialogues. a comprehensive review by nandwani and verma (2021) emphasized the role of cultural context in emotion perception. recent research has explored multi-label classification techniques to improve accuracy. @xcite demonstrated models that effectively capture emotion correlations, while @xcite showed that modeling label dependencies enhances emotion recognition. for low-resource languages, belay et al. (2025) introduced ethioemo, highlighting the challenges of applying pre-trained models to amharic. for amharic-specific emotion classification, @xcite focused on deep learning for analyzing social media comments, while birara (2024) evaluated lstm, bilstm, cnn, and gru models for multi-label classification. these studies demonstrate the potential of deep learning but also expose the need for better adaptation of transformer-based models for amharic. our research builds on these foundational works, focusing on low-resource amharic and applying transfer learning through labse embeddings. for model training, we utilize publicly available datasets and pre-trained models. our experiments are based on the dataset evaluating the capabilities of large language models for multi-label emotion understanding @xcite . for feature extraction and classification, we employ labse, a language-agnostic sentence embedding model, available via hugging face's sentence-transformers/labse repository. ## system overview our system is based on the language-agnostic bert sentence embedding (labse) model, a multilingual transformer-based model designed for cross-lingual sentence representations @xcite . given the nature of the semeval-2025 task 11 as a multi-label classification problem, we introduced specific modifications to adapt labse for effective emotion detection. this section outlines our model architecture, preprocessing pipeline, and key challenges, along with the solutions implemented to address them. ## conclusion this work explored multi-label emotion detection in amharic using a transfer learning approach. we fine-tuned labse with class-weighted loss and evaluated its performance on the semeval-2025 task 11 dataset. our model achieved competitive results, ranking 14th out of 43 teams, with an accuracy of 0.4938, a micro-f1 score of 0.6931, and a macro-f1 score of 0.6450. through ablation studies, we found that simple upsampling for class balancing did not improve performance, suggesting the need for more effective data augmentation techniques. error analysis revealed strong classification performance for anger and disgust but difficulties distinguishing fear and surprise, highlighting the challenge of contextdependent emotional expressions. future work can explore paraphrasing-based augmentation, adversarial training, and more adaptive loss functions to better handle class imbalance. additionally, incorporating context-aware embeddings and expanding the dataset with high-quality labeled examples could further enhance multi-label emotion detection in amharic.