CIOL at S em E val-2025 Task 11: Multilingual Pre-trained Model Fusion for Text-based Emotion Recognition (2025)

Abstract: Multilingual emotion detection is a critical challenge in natural language processing, enabling applications in sentiment analysis, mental health monitoring, and user engagement. However, existing models struggle with overlapping emotions, intensity quantification, and cross-lingual adaptation, particularly in low-resource languages. This study addresses these challenges as part of SemEval-2025 Task 11 by leveraging language-specific transformer models for multi-label classification (Track A), intensity prediction (Track B), and cross-lingual generalization (Track C). Our models achieved strong performance in Russian (Track A: 0.848 F1, Track B: 0.8594 F1) due to emotion-rich pretraining, while Chinese (0.483 F1) and Spanish (0.6848 F1) struggled with intensity estimation. Track C faced significant cross-lingual adaptation issues, with Russian (0.3102 F1), Chinese (0.2992 F1), and Indian (0.2613 F1) highlighting challenges in low-resource settings. Despite these limitations, our findings provide valuable insights into multilingual emotion detection. Future work should enhance cross-lingual representations, address data scarcity, and integrate multimodal information for improved generalization and real-world applicability.

## introduction text-based emotion detection is pivotal for ai systems analyzing digital communication, enabling applications like mental health monitoring and customer feedback analysis @xcite . the significance of @xcite lies in addressing critical gaps in existing systems: overlapping emotions, intensity quantification, and cross-lingual adaptation-limitations that hinder real-world deploy-ment @xcite . motivated by the prevalence of multi-emotion expressions (68% of social media posts, @xcite and the scarcity of robust solutions for low-resource languages, this study aims to develop a unified multilingual framework for multi-label classification, intensity prediction, and cross-lingual emotion detection. our methodology integrates pre-trained transformers tailored to each track. for multi-label classification (track a), language-specific models like distilroberta (english) and rubert (russian) leverage attention mechanisms to model emotion co-occurrence @xcite . track b combines affective lexicons with neural networks for intensity prediction, extending hybrid symbolic-neural frameworks @xcite , while track c employs multilingual bert and synthetic data to bridge low-resource language gaps @xcite . key findings reveal that multi-label models excel at detecting joy-surprise combinations (0.83 f1) but falter with linguistically ambiguous pairs like anger-disgust (0.61 f1) @xcite . intensity prediction models show robustness to sarcasm (0.68 human correlation) but require cultural calibration to address expression norms @xcite . cross-lingual training improves low-resource language performance by 19-28% but reduces english accuracy by 7%, highlighting a trade-off between generalization and specificity @xcite . results demonstrate stark contrasts: russian models dominate tracks a (0.848 f1) and b (0.8594 f1), benefiting from emotion-rich pretraining, while brazilian portuguese (0.2773 f1) and chinese (0.483 f1) lag due to data scarcity and morphological complexity. cross-lingual tasks (track c: 0.26-0.31 f1) expose challenges in syntactic divergence, particularly for indian languages. implementation struggles include 38% higher data demands for multi-label models, annotation inconsistencies (krippendorff's α: 0.54-0.83), and inference latency (420ms per sample), underscoring the tension between psychological validity and computational practicality. semeval 2025 task 11 (muhammad et al., 2025b) introduces text-based emotion detection through three distinct tracks: multi-label classification (track a), intensity prediction (track b), and crosslingual transfer (track c). track a builds on earlier efforts, such as semeval-2018 task 1 (van hee @xcite and semeval-2020 task 3 (armendariz et al., 2020), which concentrated on emotion intensity and multi-label classification, respectively. recent surveys highlight the growing demand for multilingual emotion detection, particularly for under-resourced languages @xcite . task a addresses this by requiring systems to handle english, brazilian portuguese, and russian, bridging gaps in prior work that centered on english @xcite . our approach differs from cross-lingual methods like semeval-2022 task 8 (chen and zhao, 2022), which used machine-translated data. instead, we fine-tune language-specific transformers on native datasets, aligning with findings that they outperform translation-based models in low-resource settings @xcite . public datasets like semeval-2022 task 8 @xcite and goemotions (garg and ramakrishnan, 2020) support our preprocessing. unlike lexicon-based studies, we integrate pretrained emotion priors from task-specific transformers, leveraging embeddingdriven label coherence @xcite . our unified framework combines a language-agnostic pipeline with tailored backbones, balancing scalability and linguistic specificity over monolithic multilingual models @xcite . ## system overview semeval 2025 task 11 advances text-based emotion detection through three tracks @xcite . track a focuses on multi-label emotion classification across english (eng), brazilian portuguese (ptbr), and russian (rus) using predefined emotion labels. track b addresses emotion intensity prediction by assigning numerical scores to quantify emotional strength, while track c explores cross-lingual generalization by transferring emotion detection models between languages. our system for track a (multi-label emotion detection) fine-tunes language-specific transformer models on emotion-annotated text, leveraging their pretrained linguistic and emotion-centric priors. for english, we use j-hartmann/emotion-english-distilroberta-base @xcite , optimized for emotion analysis. brazilian portuguese employs hate-speech-cnerg/dehatebert-mono-portuguese (aluru et al., 2020), which encodes hate speech and emotion cues, while russian utilizes maxkazak/rubertbase-russian-emotion-detection (maxkazak), trained on russian social media data. our system for track b (emotion intensity prediction) fine-tunes language-specific transformer models on emotion-annotated text, leveraging their pretrained linguistic and emotion-centric priors. for russian, we use rubert, a bert-based model fine-tuned on djacon/ru_goemotions for russian emotion classification, with 178 million parameters. for chinese, we employ two models: jjlmsy/bert-basechinese-finetuned-emotion (emobert-cn) and johnson8187/chinese-emotion-small (miniemo-cn) @xcite . for spanish, our architecture combines daveni/twitter-xlm-robertaemotion-es (xlm-twitter-emoes) @xcite with finiteautomata/beto-emotion-analysis (beto-emotion) (del arco et al., 2020), a beto-based model fine-tuned on the tass 2020 task 2 corpus for multi-class emotion detection. for track c (multi-label emotion detection on cross-lingual generalization), our system fine-tunes language-specific transformer models on emotion-annotated text, leveraging their pretrained linguistic and emotion-centric priors. for russian, we use panagath/bertbase-multilingual-cased-finetuned-emotion (emotionbert-mbilingual-finetuned) @xcite , a model optimized for emotion analysis. in chinese, the same model is employed to capture both hate speech and emotion-related cues, while for indonesian, it is utilized with the advantage of prior training on russian social media data. model architecture: each model processes input text through its transformer backbone, generating contextual embeddings from the final layer. these embeddings pass through a two-layer mlp (786 → 512 units) with relu activation and dropout (0.3). for multi-label classification, we compute independent probabilities for each emotion, p @xmath0, where z i is the logit for emotion i and σ denotes the sigmoid function. predictions are thresholded at 0.5, treating each emotion as a binary task. model variants: we test variations in mlp depth (2-3 layers), hidden dimensions (512-1024), and dropout rates (0.2-0.5). the final configuration uses fixed hyperparameters across languages, differing only in the transformer backbone to preserve linguistic specificity. ## experimental setup data splits: for track a (english, brazilian portuguese, russian), track b (russian, chinese, spanish) and track c (russian, chinese, indian), predefined train, dev, and test splits are used for each language dataset. the dev set validates hyperparameter tuning (e.g., learning rate, dropout) and enables early stopping, while the final model trains exclusively on the original train split without incorporating dev data.( muhammad et al., 2025a) preprocessing & training: we tokenize texts using language-specific pretrained tokenizers (distilroberta-base, dehatebert-mono-portuguese, rubert-base-russian) with fixed sequence lengths (128 for track a; 512 for russian, 256 for chinese/spanish in track b, 128 for track c), replacing non-string entries with empty strings in track b. to address class imbalance, we oversample underrepresented labels during training. for track a, we train models using bcewithlogit-sloss, the adam optimizer (lr 1e-4), a batch size of 16, and a two-layer mlp (786→512 units) with 0.3 dropout over 50 epochs. in track b, we encode russian labels as binary multi-label vectors and chinese/spanish labels as ordinal intensity vectors (0-3). we concatenate russian [cls] embeddings (768d, rubert) with 1,000d bag of words features and fuse dual-transformer [cls] embeddings (1,536d) for chinese/spanish. for russian and chinese, we implement two-layer mlps (1,024→786 units, relu, dropout 0.3/0.5), while for spanish, we design a three-layer mlp (786→512 units, dropout 0.4) to output 24 logits (6 emotions × 4 intensities). we train all track b models using a custom multilabelmulticlassloss (per-label crossentropy), adam (lr 1e-4, weight decay 1e-5), 50-150 epochs, and batch sizes of 16 (russian/spanish) or 32 (chinese), selecting the best model via macro-averaged f1 scores and training exclusively on original splits. in track c, we used the portuguese (brazilian) dataset to train the model and predicted the emotions on russian, chinese and indonesian dataset. for the best results, we used seed 42, max length of 128, batch size of 8, epoch 5 and hidden dimensions @xcite with a learning rate of 0.001 and a dropout of 0.3. ## conclusion this study explored multilingual, multilabel emotion detection and intensity prediction in semeval-2025 task 11 using language-specific transformers. track a excelled in russian due to emotion-rich pretraining, while portuguese struggled with data scarcity, and english faced challenges with overlapping emotions. track b showed strong russian performance, but chinese and spanish suffered from misclassifications and intensity estimation issues. track c highlighted cross-lingual adaptation difficulties, particularly in low-resource languages. future work should refine cross-lingual representations, address linguistic and cultural nuances, and enhance low-resource performance. integrating multimodal data like audio and facial expressions could further enrich emotion recognition.