Z ero_ S hot at S em E val-2025 Task 11: Fine-Tuning Deep Learning and Transformer-based Models for Emotion Detection in Multi-label Classification, Intensity Estimation, and Cross-lingual Adaptation (2025)

Abstract: Language is a rich medium employed to convey emotions subtly and intricately, as abundant as human emotional experiences themselves. Emotion recognition in natural language processing (NLP) is now a core element in facilitating human-computer interaction and interpreting intricate human behavior via text. It has potential applications in every sector i.e., sentiment analysis, mental health surveillance. However, prior research on emotion recognition is primarily from high-resource languages while low-resource languages (LRLs) are not well represented. This disparity has been a limitation to the development of universally applicable emotion detection models. To address this, the SemEval-2025 Shared Task 11 focused on perceived emotions, aiming to identify the emotions conveyed by a text snippet. It includes three tracks: Multi-label Emotion Detection (Track A), Emotion Intensity (Track B), and Cross-lingual Emotion Detection (Track C). This paper explores various models, including machine learning (LR, SVM, RF, NB), deep learning (BiLSTM+CNN, BiLSTM+BiGRU), and transformer-based models (XLM-R, mBERT, ModernBERT). The results showed that XLM-R outperformed other models in Tracks A and B, while BiLSTM+CNN performed better for Track C across most languages.

## introduction language, as a means of communication, plays a central role in the conveyance and perception of emotions @xcite . emotions are an intrinsic part of human communication, affecting how we perceive and respond to others' messages. although we all feel and deal with emotions daily, the detection of emotions in text remains a challenging task in nlp @xcite . emotions are difficult to convey explicitly, * authors contributed equally to this work. and the ways people perceive and express emotions vary greatly, with differences in culture, context, and personality @xcite @xcite . therefore, emotion detection from text is one of the most difficult tasks in nlp. the ability to detect emotions from text is increasingly crucial for applications such as virtual assistants, mental health monitoring systems, and social media analytics @xcite . the majority of existing studies on emotion detection have focused on high-resource languages, which are supported by large datasets and extensive research. this focus has created an enormous research gap in terms of low-resource languages, which lack high-quality annotated data @xcite . to address these challenges, the semeval-2025 shared task 11 titled bridging the gap in text-based emotion detection foot_0 focused on approximately 32 low-resource languages, including afrikaans (afr), amharic (amh), oromo (orm), hausa (hau), among others @xcite . the task includes three tracks: multi-label and cross-lingual emotion detection, and detection of emotion intensity. these challenges involve processing textual features to grasp the hidden meaning in cultural and linguistic contexts and classify the text into 6 classes i.e., joy, sadness, fear, anger, surprise, or disgust simultaneously in track a, detecting emotion intensity as no emotion, low, moderate, or high in track b, and adapting the model to multiple languages in track c. therefore, the contributions of this work are as follows: the implementation details of the tasks will be found in the github repository 2 . the rest of the paper is organized as follows: section 2 discusses related work, section 3 describes the dataset and task, section 4 outlines the system overview, section 5 presents the results analysis, section 6 summarizes insights and future research directions, and section 7 outlines the limitations of our research. ## related work multi-label emotion detection is essential for discerning complex emotional states from text, where instances may display multiple emotions concurrently. ## dataset and task description the shared task on emotion detection consists of three tracks, namely track a (multi-label emotion classification), track b (emotion intensity prediction), and track c (cross-lingual emotion detection). track a predicts perceived emotions (joy, sadness, fear, anger, surprise, disgust) in a text, with disgust excluded for some languages. track b assigns an intensity level (no, low, moderate, high) to each emotion. the dataset @xcite for this track provides labeled instances indicating the degree of emotion expressed in the text. finally, track c focuses on cross-lingual emotion detection, requiring models to classify emotions in a target language using a labeled dataset from another language. it lacks a labeled training corpus as compared to other tracks. the dataset structure remains consistent across tracks, allowing exploration of multi-label classification, intensity prediction, and cross-lingual generalization. tables a.1, a.2, and a.3 in appendix a show the class-wise distribution of train, validation, and test set for these tasks. ## system overview figure 1 illustrates the exploration of various ml, dl, and transformer-based models to develop a framework for detecting multi-label and crosslingual emotion, along with emotion intensity estimation. ## result analysis ## limitations although the study presents valuable information on emotion detection in lrls, certain limitations inevitably affect the generalizability and robustness of its findings. table a.1 and a.2 show data for languages like marathi, hindi, russian, amharic, igbo, and algerian arabic, and large imbalances in class distribution. for example, in amharic, there are 1268 samples for disgust but only 109 for the fear class. furthermore, table a.3 is extended to include surprise as an additional emotion class and covers amharic, algerian arabic, hausa, oromo, and somali. class imbalance is particularly evident in languages like oromo, where joy (547 samples) dwarfs fear (65 samples). the tables also contain w t values that are the total number of words in the training set, higher for more-resourced languages (e.g., 69926 for amharic in table a.3) than lower-resourced languages (e.g., 2911 for fear in oromo).