Domain_adaptation at S em E val-2025 Task 11: Adversarial Domain Adaptation for Text-based Emotion Recognition (2025)

Abstract: We report our participation in the SemEval-2025 shared task on classification of emotions and describe our solutions with BERT-based models and their ensembles. We participate in tracks A and B. We apply and compare base XLM-RoBERTa, Adversarial Domain Adaptation (ADA) on the XLM-RoBERTa with text length as the adversarial feature. As a simple baseline we also use a Logistic Regression based on tf-idf features. We show that the usage of ADA increases the f1 macro score on the low-resource languages, and on the texts of lower length. Besides, we describe our approach to tracks A and C where we use ADA with the text language as the confounder. We show that for some languages it helps to improve the f1 score. In all the tracks we work with the following languages: Russian, Amharic, Algerian Arabic, German, English, Spanish, Hausa, Brasilian Portuguese, Romanian, Ukrainian.

## introduction non-topical text classification includes a wide range of tasks aimed at predicting a text property that is not connected directly to a text topic. for example, predicting a text style, politeness , difficulty level, the age or the first language of its author, etc. it is applied in many areas such as information retrieval, language teaching, or linguistic research. @xcite introduced bert -(bidirectional encoder representations from transformers), an efficient language representation model based on the transformer architecture @xcite . it achieves state-of-the-art results for various nlp tasks, including text classification. xlm-roberta @xcite is an improved variant of bert. it has a similar architecture but uses a bigger and more genre-diverse corpus based on common crawl (instead of wikipedia for the multilingual bert). therefore, we choose xlm-roberta as the classifier for the experiments in our research. one of the most significant problems in text classification is distribution shifts, such as topical shifts, shifts in text length, or the distribution of languages. for example, @xcite shows the effect of topical shifts for genre classification. if a topic is more frequent in the training corpus for a given target class, then a classifier tends to predict the target class by the keywords of the topic. this causes numerous unreasonable mistakes in text classification. one of the algorithms that could be helpful to mitigate topical shifts is adversarial domain adaptation (ada) @xcite . it uses an adversarial loss to make the classification features less dependent on the domain of the training data. it supposes training a feature extractor, a domain discriminator, and a target classifier. the feature extractor and target classifier are trained to achieve high accuracy for the classification of the target class and at the same time deceive the domain discriminator to make it impossible to differentiate two domains. in contrast, the domain discriminator intends to classify the text domain correctly. there was a lot of research on text-based emotion classification in recent years. some of them use classical ml approaches. for example, @xcite adjust the multi-label k-nearest neighbors (mlknn) classifier to allow iterative corrections of the multi-label emotion classification. in this study, we report our participation in se-meval 2025 task 11 @xcite . we train xlm-roberta base and try to improve its performance with addition of adversarial domain adaptation @xcite . ## related work non-topical text classification is not a new task. for example, numerous attempts have appeared to build a precise classifier of genres based on various architectures from linear discrimination (karlgren most state-of-the art results in the domain of nlp were achieved with transformer-based architectures. @xcite gives important advices on how to apply the bert architecture to the task of text classification. we use the recommended values of learning rate and the number of epochs in our study. the task of emotion classification is also wellknown and widely researched. for example, in (rasouli and kiani, 2023) the authors apply a bertbased transfer learning approach to achieve high accuracy on the short persian texts. however, their study does not include usage and analysis of the adversarial methods in contrast to ours. @xcite modify adversarial domain adaptation (ada) and present a novel approach for domain adaptation. the methods are applied and compared on the tasks of sentiment analysis and yes-no binary questino answering. although their results surpasses other techniques compared in their study, the authors mostly work with much longer texts then we do in our study. regarding the shortness of the texts provided in the semeval 2025 shared task 11, it cannot be guaranteed that the novel methods are able to significantly overpass the simplier ones. ## data analysis before making any experiments, we look at the given data to mention some patterns which could be helpful for building robust classifiers. all the data we use in our study is provided by the semeval 2025 shared task 11 organizers @xcite . the dev and test data contain a wide range of languages including the rare ones. for example, it contains the ethiopian languages (amharic, oromo, somali, and tigrinya) @xcite . in ## conclusions and future research we show that: adversarial methods are potentially helpful to achieve higher quality in a wide range of tasks and to combat various distribution shifts, including classification of emotions. however, in order to utilize the whole capacity of the adversarial methods, it would be helpful to use models with a higher number of parameters. for example, best results in the semeval-2025 task11 competition @xcite were achieved using llms. however, due to limited computing resources, we did not have the opportunity to fine-tune large language models using the adversarial methods. therefore, there is still a room for improvement. in the future, using ada in conjunction with large language models could make it possible to obtain much more accurate and reliable classifiers. in addition, it might be useful to try more modern competitive domain adaptation methods, such as energy-based adversarial domain adaptation (eada).