This paper introduces our participating system to the Explainable Detection of Online Sexism (EDOS) SemEval-2023 - Task 10: Explainable Detection of Online Sexism. The EDOS shared task covers three hierarchical sub-tasks for sexism detection, coarse-grained and fine-grained categorization. We have investigated both single-task and multi-task learning based on RoBERTa transformer-based language models. For improving the results, we have performed further pre-training of RoBERTa on the provided unlabeled data. Besides, we have employed a small sample of the unlabeled data for semi-supervised learning using the minimum class-confusion loss. Our system has achieved macro F1 scores of 82.25\%, 67.35\%, and 49.8\% on Tasks A, B, and C, respectively.

In this paper, we introduce our participating system to the ArAIEval Shared Task, addressing both the detection of persuasion techniques and disinformation tasks. Our proposed system employs a pre-trained transformer-based language model for Arabic, alongside a classifier. We have assessed the performance of three Arabic Pre-trained Language Models (PLMs) for sentence encoding. Additionally, to enhance our model’s performance, we have explored various training objectives, including Cross-Entropy loss, regularized Mixup loss, asymmetric multi-label loss, and Focal Tversky loss. On the official test set, our system has achieved micro-F1 scores of 0.7515, 0.5666, 0.904, and 0.8333 for Sub-Task 1A, Sub-Task 1B, Sub-Task 2A, and Sub-Task 2B, respectively. Furthermore, our system has secured the 4th, 1st, 3rd, and 2nd positions, respectively, among all participating systems in sub-tasks 1A, 1B, 2A, and 2B of the ArAIEval shared task.

In this paper, we present our submitted system for the WojoodNER Shared Task, addressing both flat and nested Arabic Named Entity Recognition (NER). Our system is based on a BERT-based multi-task learning model that leverages the existing Arabic Pretrained Language Models (PLMs) to encode the input sentences. To enhance the performance of our model, we have employed a multi-task loss variance penalty and combined several training objectives, including the Cross-Entropy loss, the Dice loss, the Tversky loss, and the Focal loss. Besides, we have studied the performance of three existing Arabic PLMs for sentence encoding. On the official test set, our system has obtained a micro-F1 score of 0.9113 and 0.9303 for Flat (Sub-Task 1) and Nested (Sub-Task 2) NER, respectively. It has been ranked in the 6th and the 2nd positions among all participating systems in Sub-Task 1 and Sub-Task 2, respectively.

The aim of this paper is to investigate the role of Luxembourgish adjectives in expressing sentiments in user comments written at the web presence of rtl.lu (RTL is the abbreviation for Radio Television Letzebuerg). Alongside many textual features or representations,adjectives could be used in order to detect sentiment, even on a sentence or comment level. In fact, they are also by themselves one of the best ways to describe a sentiment, despite the fact that other word classes such as nouns, verbs, adverbs or conjunctions can also be utilized for this purpose. The empirical part of this study focuses on a list of adjectives that were extracted from an annotated corpus. The corpus contains the part of speech tags of individual words and sentiment annotation on the adjective, sentence and comment level. Suffixes of Luxembourgish adjectives like -esch, -eg, -lech, -al, -el, -iv, -ent, -los, -barand the prefixon- were explicitly investigated, especially by paying attention to their role in regards to building a model by applying classical machine learning techniques. We also considered the interaction of adjectives with other grammatical means, especially other part of speeches, e.g. negations, which can completely reverse the meaning, thus the sentiment of an utterance.

The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and facilitates sentence-level annotation of several annotators in parallel. In the heart of our framework is an XML database, which serves as central part linking several components. The corpus in the database consists of news articles and user comments. One of the components is LuNa, a tool for linguistic preprocessing of the data set. It tokenizes the text, splits it into sentences and assigns POS-tags to the tokens. After that, the preprocessed text is stored in XML format into the database. The Sentiment Annotation Tool, which is a browser-based tool, then enables the annotation of split sentences from the database. The Sentiment Engine, a separate module, is trained with this material in order to annotate the whole data set and analyze the sentiment of the comments over time and in relationship to the news articles. The gained knowledge can again be used to improve the sentiment classification on the one hand and on the other hand to understand the sentiment phenomenon from the linguistic point of view.

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person’s expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person’s past expressions, and offer a better understanding of the sentiment from the expresser’s perspective. Additionally, we investigate how a person’s sentiment changes over time so that recent incidents or opinions may have more effect on the person’s current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.