Masakhane-Afrisenti at S em E val-2023 Task 12: Sentiment Analysis using A fro-centric Language Models and Adapters for Low-resource A frican Languages (2023)

Abstract: Detecting harmful content on social media plat-forms is crucial in preventing the negative ef-fects these posts can have on social media users. This paper presents our methodology for tack-ling task 10 from SemEval23, which focuseson detecting and classifying online sexism insocial media posts. We constructed our solu-tion using an ensemble of transformer-basedmodels (that have been fine-tuned; BERTweet,RoBERTa, and DeBERTa). To alleviate the var-ious issues caused by the class imbalance inthe dataset provided and improve the general-ization of our model, our framework employsdata augmentation and semi-supervised learn-ing. Specifically, we use back-translation fordata augmentation in two scenarios: augment-ing the underrepresented class and augment-ing all classes. In this study, we analyze theimpact of these different strategies on the sys-tem’s overall performance and determine whichtechnique is the most effective. Extensive ex-periments demonstrate the efficacy of our ap-proach. For sub-task A, the system achievedan F1-score of 0.8613. The source code to re-produce the proposed solutions is available onGithub

## introduction low-resourced languages receive less attention in natural language processing (nlp) research because they lack the quality of datasets necessary for training, evaluation, and model implementation. however, the increasing abundance of social media platforms on the internet is changing the game for these languages, giving them more visibility and accessibility. this was not always the case, as a significant amount of nlp research focused on sentiment analysis and other tasks geared toward specific languages having a high online presence, thus resulting in the creation of techniques and models tailored to their needs @xcite . the rise of region-specific competitions such as the afrisenti-semeval competition has led to ef- † these authors contributed equally to this work. this paper details our submission for the afrisenti semeval-2023 task 12 @xcite , which investigates the effectiveness of pre-trained afro-centric language models and adapters for sentiment classification tasks. our work explores the potential of these models and adapters to enhance sentiment analysis performance in the context of african languages and cultures. our codes and all project materials are publicly available foot_0 . ## background sentiment analysis, a crucial task in natural language processing employs machine learning techniques to identify emotions in text, thus having numerous practical applications in areas such as public health, business, governance, psychology, and more @xcite . the benefits of sentiment analysis are diverse with its effect felt on almost every aspect of human endeavor @xcite . while low-resource languages have been neglected in sentiment analysis research, previous works by @xcite , @xcite and @xcite have created differ-ent sentiment corpora for nigerian and ethiopian languages. with text classification used for benchmarking most pre-trained language models, such as @xcite and @xcite , their effectiveness on the downstream task of sentiment classification is yet to be properly explored. the dataset introduced by @xcite in semeval 2023 task 12, the first afrocentric semeval shared task, includes 14 african languages divided into three sub-tasks: languagespecific, multilingual, and zero-shot competitions. as part of our contribution, our team participated in all sub-tasks of the shared task, explored the effectiveness of several pre-trained networks trained on african languages, and examined the use of parameter-efficient approaches like adapters @xcite for a zero-shot classification task. ## conclusion in this paper, we presented our submission for the afrisenti-semeval shared task 12 of semeval-2023, focusing on the classification of sentiment for monolingual, multilingual, and zero-shot settings for low-resource african languages. we explored afro-centric, language-specific, and general pretrained language models for fine-tuning. based on the test result, for monolingual sentiment classification, afro-centric language models showed better performance for most of the languages. however, language-specific pre-trained language models perform better than afro-centric language models for algerian arabic and morrocan darija. for multilingual sentiment classification, afro-centric language models show promising results. for the zeroshot task, we see that using adapters shows promising results for related languages while afro-centric languages show better performance for unrelated languages. we would like to further our research on adapters for low-resource languages as it shows promising results in the zero-shot setting for related languages.