GOLDX at S em E val-2025 Task 11: R o BERT a for Text-Based Emotion Detection (2025)

Abstract: This is an ensemble of RoBERTa models based approach to classify emotions from texts.

## introduction the semeval-2025 task 11: bridging the gap in text-based emotion detection focuses on multilabel emotion detection, which is a key challenge in the natural language processing (nlp) world. its applications include sentiment analysis, user engagement analysis, and mental health monitoring. this task involves predicting the presence or absence of specific emotions (in the form of 0 or 1) for a given text. track a requires detecting five different emotions: anger, fear, joy, sadness, and surprise, using a labeled dataset in a single language (for english). track c, on the other hand, introduces a cross-lingual setting where no labeled training data is provided in the target language. moreover, track c includes an additional emotion category, which is disgust, increasing the total number of labels to six in certain languages. participants must develop strategies to generalize across languages without access to direct supervision in the target language. in this paper, the approach leverages an ensemble of roberta models, each trained with different hyperparameters, to improve the robustness and performance. for track a, the training is done using the provided labeled dataset (english). for track c, a cross-lingual transfer learning strategy is adopted (training on chinese data and inferring on indonesian). additionally, marianmt, a neural machine translation model developed by helsinki-nlp, is employed to translate non-english data before classification, helping bridge the linguistic gap. through this participation, strong results are achieved, obtaining a macro-f1 score of 0.762 for track a and 0.4291 for track c. this system performed competitively relative to other submissions, demonstrating the effectiveness of model ensembling and translation-based adaptation. ## results the system is evaluated in two phases: