NTA at S em E val-2025 Task 11: Enhanced Multilingual Textual Multi-label Emotion Detection via Integrated Augmentation Learning (2025)

Abstract: Emotion detection in text is crucial for various applications, but progress, especially in multi-label scenarios, is often hampered by data scarcity, particularly for low-resource languages like Emakhuwa and Tigrinya. This lack of data limits model performance and generalizability. To address this, the NTA team developed a system for SemEval-2025 Task 11, leveraging data augmentation techniques: swap, deletion, oversampling, emotion-focused synonym insertion and synonym replacement to enhance baseline models for multilingual textual multi-label emotion detection. Our proposed system achieved significantly higher macro F1-scores compared to the baseline across multiple languages, demonstrating a robust approach to tackling data scarcity. This resulted in a 17th place overall ranking on the private leaderboard, and remarkably, we achieved the highest score and became the winner in Tigrinya language, demonstrating the effectiveness of our approach in a low-resource setting.

## introduction emotions are influential in human interactions because it affects how people relate to each other, communicate, make decisions, and even sustain mental health. being able to notice and understand emotions expressed in language is particularly important for the development of adaptive human-machine interfaces, ai systems with emotional intelligence, and targeted mental health care though combating depression. while there has been progress in emotion recognition, it is still deficient in most languages such as english, which happens to dominate the world. leaving the bulk of the world's ethnolinguistic diversity unattended creates gaps that cannot be automatically filled by western emotional understanding. semeval 2025 task 11 "bridging the gap in text-based emotion detection" @xcite attempts to solve this problem using multilanguage emotion recognition for 32 african, asian and european languages such as emakhuwa, amharic, hausa and swahili. the task mainly focuses on the recognition of insights emotions , that is, the emotion a listener infers from the speaker's words and takes into consideration the interactions surrounding it. the participants work in three tracks. track a: multilabel emotions, track b: emotion intensity, track c: cross-lingual emotion detection. we focus on track a, multi-label emotions. we identify the emotion(s) portrayed by the speaker in the given target text snippet using transformer models such as deberta, roberta, and mxlm-r, as these are powerful models from different linguistic families. to mitigate the lack of training data, we deploy augmentation strategies such as synonym replacement, deletion of random words, and oversampling for the minority emotion classes. these techniques improve model performance for languages with lower availability of resources. the analysis shows positive changes in macro-f1 scores, demonstrating the benefit of large language models in combination with advanced data for cross-cultural emotion detection. this not only solves the problem of language diversity, but also enhances the availability of emotion-sensitive technologies around the world. ## related work emotion detection has evolved rapidly, fueled by transformer models (bert, roberta) that capture context better than old lexicon-based tools or handcrafted features @xcite . multilingual efforts, like xlm-r @xcite , struggle with low-resource languages-african languages, for instance, still lag despite adaptations like afro-xlm-r @xcite . adding to the chaos, multi-label emotion tasks require tweaked models to handle overlapping feelings. data scarcity remains a hurdle. while methods like eda @xcite randomly swap or delete words, our hybrid approach blends tailored augmentation with model adjustments, aiming to preserve cultural and linguistic quirks often lost in translation. ## shared task description the semeval-2025 task 11 focuses on text-based emotion detection, specifically identifying the perceived emotion of a speaker based on a short text snippet. it consists of three tracks, however, we only focus on track a: multi-label emotion detection: given a target text snippet, predict the perceived emotion(s) of the speaker. specifically, select whether each of the following emotions apply: joy, sadness, fear, anger, surprise, or disgust. in other words, label the text snippet with: joy (1) or no joy (0), sadness (1) or no sadness (0), anger (1) or no anger (0), surprise (1) or no surprise (0), and disgust (1) or no disgust (0). note that for some languages such as english, the set perceived emotions includes 5 emotions: joy, sadness, fear, anger, or surprise and does not include disgust. a training dataset with gold emotion labels will be provided for this track. the dataset for this shared task @xcite is drawn from five multilingual sources: social media posts, personal narratives, talks/speeches, literary texts, and news data, including both human-written and machine-generated content. this track comprises 28 languages from various countries in africa, asia, and europe, including: afrikaans (afr), algerian arabic (arq), amharic (amh), chinese (chn), emakhuwa (vmw), english (eng), german (deu), hausa (hau), hindi (hin), igbo (ibo), kinyarwanda (kin), marathi (mar), moroccan arabic (ary), nigerian pidgin (pcm), oromo (orm), brazilian portuguese (ptbr), mozambican portuguese (ptmz), romanian (ron), russian (rus), somali (som), latin american spanish (esp), sundanese (sun), swahili (swa), swedish (swe), tatar (tat), tigrinya (tir), ukrainian (ukr), and yoruba (yor). ## system overview the architecture of our system is illustrated in figure 1 . the pipeline is divided into five main steps: preprocessing, data augmentation, fine-tuning, voting scheme and threshold optimization. the detailed structure of the pipeline is depicted in the following subsections below. ## experimental setup we present our results in two subsections. the first subsection compares results with and without augmentation, and the second compares official results and the semeval baseline. ## conclusion this study addresses multi-label emotion detection in under-resourced languages using a hybrid approach that combines targeted data augmentation (lexical normalization, synonym expansion) with language-specific model ensembles. the system demonstrated robust performance, effectively preventing data bias and outperforming baselines overall. while successful, improvements are still needed for certain languages. future work will focus on experimenting with additional models and augmentation techniques to further enhance performance.