XLM -Muriel at S em E val-2025 Task 11: Hard Parameter Sharing for Multi-lingual Multi-label Emotion Detection (2025)

Abstract: Throughout this paper we present our system developed to solve SemEval-2025 Task 11: Bridging the Gap in Text-based Emotion Detection Track A. To participate in this contest, we use an architecture based on a pretrained encoder model as the shared part of the model and then add specific head to adapt the shared part for each language. In the first part of this report, we will introduce the task and the specific track in which we participated and then elaborate on the dataset and the system we developed to handle the task. Finally, we will analyze our results and discuss limitations and potential strength point of our solution that could be leveraged in future work to improve results on similar tasks.

## introduction emotions are at once an everyday experience and an elusive phenomenon. although we routinely express and regulate our feelings, they remain intricate and subtle, often defying clear articulation. language itself is employed in remarkably nuanced ways to convey these internal states, as noted by previous research @xcite @xcite . moreover, individuals differ greatly in both how they perceive and display their emotions-even among those sharing similar cultural or social backgrounds-making it impossible to determine someone's true emotional state with complete certainty based solely on their words. emotion recognition, therefore, is not a singular task but rather a collection of related challenges. it includes identifying the speaker's emotional state, discerning the sentiment a piece of text conveys, and even gauging the emotional response it triggers in a reader. our focus here is on perceived emotion: inferring the emotion that the majority would attribute to the speaker based on a brief sentence or text snippet. this task deliberately excludes determining the reader's emotional reaction, the emotion of another person mentioned, or the speaker's actual feeling-since the latter remains indeterminate from limited text. this distinction is critical, as factors like cultural context, individual differences, and the inherent limitations of textual communication often cause perceived emotions to differ from the speaker's real emotional state @xcite @xcite . in this paper, we describe our system developed for track a of the task, which is designed to determine multi-label classifications for a single text snippet across all 28 languages @xcite . our system leverages a shared multilingual encoder coupled with languagespecific classification heads, enabling it to capture both universal and language-dependent emotional cues @xcite @xcite . by utilizing advanced transformer models (such as xlm-roberta) as the backbone, our approach not only processes input text efficiently but also generates predictions for multiple emotion classes-including anger, disgust, fear, joy, sadness, and surprise-for each language simultaneously. this robust design addresses the inherent challenges of multilingual emotion recognition, offering a comprehensive solution that can be adapted to varied linguistic contexts. ## related works research on multi-label emotion detection has accelerated in recent years. @xcite use emotion-specific feature extractors and label correlation graphs; @xcite propose a multitask framework for depression, sentiment, and emotion; @xcite ) leverage adversarial multi-task learning for label dependencies. more recent soft-sharing architectures and mixture-of-experts models @xcite dynamically allocate capacity per language, yielding gains on typologically distant languages at the cost of increased compute. however, these methods can overfit low-resource languages when class distributions are highly skewed and have not been evaluated in a truly massively multilingual, multi-label setting. ## brighter dataset the brighter dataset @xcite is a comprehensive collection of multilabeled emotion-annotated texts spanning 28 different languages. recognizing that most emotion recognition research has focused on high-resource languages, the brighter dataset addresses this gap by incorporating predominantly low-resource languages from africa, asia, eastern europe, and latin america. each text instance is carefully annotated by fluent speakers from various domains, capturing the subtle and complex ways in which people express emotions. the dataset not only facilitates monolingual and cross-lingual multi-label emotion identification but also supports intensity-level emotion recognition. the data collection and annotation processes for brighter were designed to overcome the inherent challenges of building highquality emotion datasets in diverse linguistic settings. by employing rigorous annotation guidelines and leveraging domain expertise, the creators of brighter have provided a valuable resource that highlights the variability in emotional expression across different cultures and text domains. experimental results presented in the associated work demonstrate significant performance differences when using or not using large language models, emphasizing the importance of this resource in bridging the gap in text-based emotion recognition research. ultimately, the brighter dataset stands as an essential step toward more inclusive and effective emotion recognition solutions in natural language processing. ## system overview to tackle the classification nature of this task, we opted for a bert-family model-specifically, xlm-roberta-large-as our backbone. we selected xlm-roberta-large because it outperforms its base variant by 17 percentage points in macro f1 on the brighter dev set ( ## experimental setup our experimental framework is designed to maximize data utilization under existing hardware constraints. we use a batch size of 1024 and restrict the maximum number of input tokens to 128, ensuring efficient attention mask construction while avoiding information loss from overly lengthy inputs. initially, the pre-trained model and tokenizer are loaded, and the encoder's weights are frozen so that only the output logits of the language-specific heads are trained using binary cross-entropy (bce-withlogitsloss) to handle the multi-label nature of the task. after roughly 10 epochs with a very low learning rate (on the order of 10 -7 ), we fine-tune the encoder along with the specialized heads using a higher learning rate (approximately 10 -3 ) over 3 additional epochs. in this two-step training strategy, gradients are computed solely for one head per epoch while keeping the other heads' parameters unchanged. ## results and analysis our experimental evaluation employed the model trained with the approach depicted in figure 1 right, with the results for four models summarized in the training strategy plays an even more crucial role: while training all parameters exclusively on one language may boost accuracy for that specific language, it can concurrently reduce overall model performance across languages. this suggests that a more balanced approach is necessary, either by training certain languages with separate parameters or by adopting an alternative architecture for their corresponding expert heads to avoid adverse effects on languages with greater semantic similarity in emotion expression. the results of our system are shown in ## conclusion our analysis of the training data further demonstrates that the number of instances for a given emotion (i.e., samples labeled with 1) critically influences model performance. insufficient representation for a specific emotion impedes the model's ability to extract its distinctive features, leading to poor detection-as observed in languages like emakhuwa and yoruba, where challenging classes such as fear and disgust are underrepresented. despite these challenges, our model achieved a highly competitive standing on the development leaderboard. this outcome underscores both the strengths of our approach and the potential for further optimization. future work should consider strategies such as increasing model size, fine-tuning hyperparameters more effectively, and employing advanced