Svandiela @ HaSpeeDe: Detecting Hate Speech in Italian Twitter Data with BERT

This paper explains the system developed for the Hate Speech Detection (HaSpeeDe) shared task within the 7th evaluation campaign EVALITA 2020 (Basile et al. 2020). The task solution proposed in this work is based on a fine-tuned BERT model. In cross-corpus evaluation, our model reached an F1 score of 77,56% on the tweets test set, and 60,31% on the news headlines test set.Questo articolo spiega il sistema sviluppato per il tesk finalizzato all’individuazione dei discorsi d’odio all’interno della campagna di valutazione EVALITA 2020 (Basile et al. 2020). La soluzione proposta per il task è basata su un raffinemento di un modello BERT. Nella valutazione finale il nostro modello raggiunge un valore F1 di 77,56% sul dataset di tweets e di 60,31% sul dataset di titoli di giornale

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)

A critical appraisal on deep neural networks:Bridge the gap between deep learning and neuroscience via XAI

Starting in the early 1940s, artificial intelligence (AI) has come a long way, and today, AI is a powerful research area with many possibilities. Deep neural networks (DNNs) are part of AI and consist of several layers-the input layers, the so-called hidden layers, and the output layers. The input layers receive data; the data are then converted into computable variables (i.e., vectors) and are passed on to the hidden layers, where they are computed. Each data point (neuron) is connected to another data point within a different layer that passes information back and forth. Adjusting the weights and bias at each hidden layer (having several iterations between those layers), such a network maps the input to output, thereby generalizing (learning) its knowledge. At the end, the deep neural network should have enough input to predict results for specific tasks successfully. The history of DNNs or neural networks is, in general, closely related to neuroscience, as the motivation of AI is to teach human intelligence to a machine. Thus, it is possible to use the knowledge of the human brain to develop algorithms that can simulate the human brain. This is performed with DNNs. The brain is considered an electrical network that sets off electrical impulses. During this process, information is carried from one synapse to another, just like it is done within neural networks. However, AI systems should be used carefully, which means that the researcher should always be capable of understanding the system he or she created, which is an issue discussed within explainable AI and DNNs