SINAI at SemEval-2020 Task 12: Offensive Language Identification Exploring Transfer Learning Models

Abstract

This paper describes the participation of SINAI team at Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. In particular, the participation in Sub-task A in English which consists of identifying tweets as offensive or not offensive. We preprocess the dataset according to the language characteristics used on social media. Then, we select a small set from the training set provided by the organizers and fine-tune different Transformerbased models in order to test their effectiveness. Our team ranks 20th out of 85 participants in Subtask-A using the XLNet model.

Attention, sentiments and emotions towards emerging climate technologies on Twitter

“…There are several taxonomies to define discrete emotions. The SemEval 2018 Task 1 dataset contains 10,000 annotated English tweets with 12 emotion classes ( Mohammad et al, 2018 ), the EmoVent dataset contains 7,303 English tweets with 8 emotion classes ( Plaza del Arco et al, 2020 ), and the GoEmotions dataset contains 58,000 annotated English reddit comments with 27 emotion classes ( Demszky et al, 2020 ). They all suggest their respective mapping to the taxonomy with seven emotion classes proposed by Ekman (1992) or group them into positive, negative, and ambiguous or neutral classes.…”

OffensEval 2023: Offensive language identification in the age of Large Language Models

“…All of the instances were based on Twitter. Most of the top systems used some pre-trained transformer-based models such as multilingual BERT, BETO and XLM-RoBERTa (Plaza-del Arco, Molina-González, and Alfonso 2021).…”

“…The best-performing teams for both sub-tasks used BETO (the Spanish version of BERT model) (Plaza-del Arco et al. 2021).…”