yasuo at HASOC2020: Fine-tune XML-RoBERTa for Hate Speech Identification

Based on the experiments performed, the pre-trained transformer-based Bert model provided better scores than other language models such as ULMFiT [11]. Bert-based models are

We used pre-trained bi-directional encoder representations using transformers (BERT) and multilingual- BERT for hate speech and offensive content detection for English, German,

Online social media platforms provide convenience for people to communicate, but the harm caused by online hate speech and offensive language accompanying them is also significant.

In this paper, we presented a system to detect hate speech and offensive language for the English language, which uses a method of multi-model ensemble learning for identifying

As mentioned in the third paragraph of the Introduction(§1), in this paper, we use two pre-trained language models based on the Transformer architecture: BERT and ALBERT to complete

In this paper, we, team MUM, propose an ensemble of Machine Learning (ML) algorithms for hate speech and offensive content identification in Indo-European languages namely,

A fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model is proposed to identify hate and offensive contents from the social media posts.. The

In the system implementation, we have used a special type of Recurrent Neural Network (RNN) based deep learning approach known as Long Short Term Memory (LSTM) [10, 11] to detect