In this paper, we propose a methodology fortask 10 of SemEval23, focusing on detectingand classifying online sexism in social me-dia posts. The task is tackling a serious is-sue, as detecting harmful content on socialmedia platforms is crucial for mitigating theharm of these posts on users. Our solutionfor this task is based on an ensemble of fine-tuned transformer-based models (BERTweet,RoBERTa, and DeBERTa). To alleviate prob-lems related to class imbalance, and to improvethe generalization capability of our model, wealso experiment with data augmentation andsemi-supervised learning. In particular, fordata augmentation, we use back-translation, ei-ther on all classes, or on the underrepresentedclasses only. We analyze the impact of thesestrategies on the overall performance of thepipeline through extensive experiments. whilefor semi-supervised learning, we found thatwith a substantial amount of unlabelled, in-domain data available, semi-supervised learn-ing can enhance the performance of certainmodels. Our proposed method (for which thesource code is available on Github12) attainsan F 1-score of 0.8613 for sub-taskA, whichranked us 10th in the competition.

Depression is a common mental disorder that severely affects the quality of life, and can lead to suicide. When diagnosed in time, mild, moderate, and even severe depression can be treated. This is why it is vital to detect signs of depression in time. One possibility for this is the use of text classification models on social media posts. Transformers have achieved state-of-the-art performance on a variety of similar text classification tasks. One drawback, however, is that when the dataset is imbalanced, the performance of these models may be negatively affected. Because of this, in this paper, we examine the effect of balancing a depression detection dataset using data augmentation. In particular, we use abstractive summarization techniques for data augmentation. We examine the effect of this method on the LT-EDI-ACL2022 task. Our results show that when increasing the multiplicity of the minority classes to the right degree, this data augmentation method can in fact improve classification scores on the task.

The ongoing COVID-19 pandemic has brought online education to the forefront of pedagogical discussions. To make this increased interest sustainable in a post-pandemic era, online courses must be built on strong pedagogical foundations. With a long history of pedagogic research, there are many principles, frameworks, and models available to help teachers in doing so. These models cover different teaching perspectives, such as constructive alignment, feedback, and the learning environment. In this paper, we discuss how we designed and implemented our online Natural Language Processing (NLP) course following constructive alignment and adhering to the pedagogical principles of LTU. By examining our course and analyzing student evaluation forms, we show that we have met our goal and successfully delivered the course. Furthermore, we discuss the additional benefits resulting from the current mode of delivery, including the increased reusability of course content and increased potential for collaboration between universities. Lastly, we also discuss where we can and will further improve the current course design.

Hate speech detection on social media platforms is crucial as it helps to avoid severe situations, and severe harm to marginalized people and groups. The application of Natural Language Processing(NLP) and Deep Learning has garnered encouraging results in the task of hate speech detection. The expression of hate, however is varied and ever evolving. Thus, it is important for better detection systems to adapt to this variance. Because of this, researchers keep on collecting data and regularly come up with hate speech detection competitions. In this paper, we discuss our entry to one such competition, namely the English version of sub-task A for the OffensEval competition. Our contribution can be perceived through our results, which were first a F1-score of 0.9089, and with further refinements described here climb up to0.9166. It serves to give more support to our hypothesis that one of the variants of BERT (Devlin et al., 2018), namely RoBERTa can successfully differentiate between offensive and not-offensive tweets, given some preprocessing steps (also outlined here).