DH-FBK @ HaSpeeDe2: Italian Hate Speech Detection via Self-Training and Oversampling

We describe in this paper the system submitted by the DH-FBK team to the HaSpeeDe evaluation task, and dealing with Italian hate speech detection (Task A). While we adopt a standard approach for fine-tuning AlBERTo, the Italian BERT model trained on tweets, we propose to improve the final classification performance by two additional steps, i.e. self-training and oversampling. Indeed, we extend the initial training data with additional silver data, carefully sampled from domain-specific tweets and obtained after first training our system only with the task training data. Then, we re-train the classifier by merging silver and task training data but oversampling the latter, so that the obtained model is more robust to possible inconsistencies in the silver data. With this configuration, we obtain a macro-averaged F1 of 0.753 on tweets, and 0.702 on news headlines

AlBERTo: Modeling Italian Social Media Language with BERT

Natural Language Processing tasks recently achieved considerable interest and progresses following the development of numerous innovative artificial intelligence models released in recent years. The increase in available computing power has made possible the application of machine learning approaches on a considerable amount of textual data, demonstrating how they can obtain very encouraging results in challenging NLP tasks by generalizing the properties of natural language directly from the data. Models such as ELMo, GPT/GPT-2, BERT, ERNIE, and RoBERTa have proved to be extremely useful in NLP tasks such as entailment, sentiment analysis, and question answering. The availability of these resources mainly in the English language motivated us towards the realization of AlBERTo, a natural language model based on BERT and trained on the Italian language. We decided to train AlBERTo from scratch on social network language, Twitter in particular, because many of the classic tasks of content analysis are oriented to data extracted from the digital sphere of users. The model was distributed to the community through a repository on GitHub and the Transformers library (Wolf et al. 2019) released by the development group huggingface.co. We have evaluated the validity of the model on the classification tasks of sentiment polarity, irony, subjectivity, and hate speech. The specifications of the model, the code developed for training and fine-tuning, and the instructions for using it in a research project are freely available

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)