Abstract

This paper describes the system we developed for SemEval 2019 on Multilingual detection of hate speech against immigrants and women in Twitter (HatEval - Task 5). We use an approach based on an Attention-based Long Short-Term Memory Recurrent Neural Network. In particular, we build a Bidirectional LSTM to extract information from the word embeddings over the sentence, then apply attention over the hidden states to estimate the importance of each word and finally feed this context vector to another LSTM model to get a representation. Then, the output obtained with this model is used to get the prediction of each of the sub-tasks.

Highlights

  • Nowadays, the number of content generated by users on social networks is growing rapidly

  • Models based on deep learning, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) have been widely used

  • This paper presents a strategy based on RNN, which is an extension of previous models proposed for the tasks MEX-A3T and EVALITA 2018 (Cuza et al, 2018; la Pena Sarracen et al, 2018)

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Summary

Introduction

The number of content generated by users on social networks is growing rapidly. In this context, the problem of detecting and limiting the dissemination of the Hate Speech is becoming a matter of great importance. Some examples are the Workshop on Trolling, Aggression and Cyberbullying (Kumar et al, 2018), that included a shared task on aggression identification; the tracks on Automatic Misogyny Identification (AMI) (Fersini et al, 2018a) and on Autohorship and Aggressiveness Analysis (MEX-A3T) (Alvarez-Carmona et al, 2018) proposed at IberEval 2018; the Automatic Misogyny Identificationtask at EVALITA 2018 (Fersini et al, 2018b), the Workshop on Abusive Language (Waseem et al, 2017) and the GermEval Shared Task on the Identification of Offensive Language (Wiegand et al, 2018). The proposed works have used different features and models. Models based on deep learning, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) have been widely used

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