CSECU -Learners at S em E val-2025 Task 11: Multilingual Emotion Recognition and Intensity Prediction with Language-tuned Transformers and Multi-sample Dropout (2025)

Abstract: In today’s digital era, individuals convey their feelings, viewpoints, and perspectives across various platforms in nuanced and intricate ways. At times, these expressions can be challenging to articulate and interpret. Emotion recognition aims to identify the most relevant emotions in a text that accurately represent the author’s psychological state. Despite its substantial impact on natural language processing (NLP), this task has primarily been researched only in high-resource languages. To bridge this gap, SemEval-2025 Task 11 introduces a multilingual emotion recognition challenge encompassing 32 languages, promoting broader linguistic inclusivity in emotion recognition. This paper presents our participation in this task, where we introduce a language-specific fine-tuned transformer-based system for emotion recognition and emotion intensity prediction. To enhance generalization, we incorporate a multi-sample dropout strategy. Our approach is evaluated across 11 languages, and experimental results demonstrate its competitive performance, achieving top-tier results in certain languages.

## introduction understanding emotions expressed in a text has gained significant attention in natural language processing (nlp) due to its wide-ranging applications in sentiment analysis, mental health monitoring, and human-computer interaction @xcite . while sentiment analysis primarily focuses on classifying text into positive, negative, or neutral categories, emotion classification provides a more granular understanding by identifying specific emotions such as joy, sadness, anger, and fear @xcite . however, though there are several research on emotion detection in mid-to high-resource languages such as english, arabic, and spanish @xcite @xcite , very few emotion recognition jobs are done in low-resource languages such as afrikaans, hausa, and romanian @xcite . to bridge this major research gap in emotion recognition, @xcite introduces a task in semeval-2025. the task consists of three different tracks. track a is classifying emotion in a sentence which is structured as a multi-label classification task. except for english and afrikaans, sentences in all other languages are required to be classified into six different emotions such as "anger", "fear", "joy", "sadness", "surprise", and "disgust". the "disgust" and "surprise" emotion classes are absent for the english and afrikaans languages respectively. when a sentence doesn't fall into any of the emotion classes it is categorized as the no emotion instance. track b is to predict the degree of intensity of each recognized emotion. track c is to predict the perceived emotion labels of a new text instance in a different target language given a labeled training set in one of the support languages. among the three tracks, we have participated in the first two. to demonstrate a clear view of the task definition, we articulate an example in ## system overview this section provides an overview of our proposed system for semeval-2025 task 11: bridging the gap in text-based emotion detection. the competition consists of three separate tracks, and we have participated in the first two. track a focuses on detecting emotions in textual data across multiple languages, while track b involves predicting emotion intensity. we have participated across 11 languages for both tracks, as summarized in given an input sentence, our system first encodes it with a language-tuned transformer @xcite . in addition to the contextual embedding from the transformer, we later use a multisample dropout @xcite procedure to improve the generalization ability of the system. to obtain final logits (unnormalized scores), we fuse the logits from different dropout samples. finally, we normalize the logits with sigmoid function @xcite and predict with global thresholding. ## discussion in this section, we estimate the impact of the multisample dropout (msd) strategy in our csecu-learners system. additionally, we compare our system's results with some state-of-the-art (sota) multilingual transformers and large language models (llms). since semeval-2025 task 11 focuses on multilingual emotion classification, we compare the performance of our system with several multilingual transformer models. large language models (llms) have recently demonstrated remarkable learning and reasoning capabilities across various downstream tasks. in ## conclusion and future direction this paper presents our proposed system for emotion recognition and intensity prediction. identifying emotions in a sentence requires more than just superficial analysis; understanding contextual meaning is essential. to address this challenge, we fine-tuned various transformer models across different languages, leveraging their ability to capture contextual embeddings. additionally, we incorporated a multi-sample dropout strategy to enhance generalization. experimental results validate the effectiveness of our proposed approach, demonstrating competitive performance in comparison to several existing methods. in future work, we plan to explore other stateof-the-art transformer architectures and investigate the fusion of multiple transformer models. since the dataset is imbalanced, we aim to incorporate weighted loss functions to improve learning across all classes.