nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection
By Jelena Mitrović, Bastian Birkeneder, Michael Granitzer. 2019
25 citations recorded by the source corpus.
This paper presents our submission for the SemEval shared task 6, sub-task A on the identification of offensive language. Our proposed model, C-BiGRU, combines a Convolutional Neural Network (CNN) with a bidirectional Recurrent Neural Network (RNN). We utilize word2vec to capture the semantic similarities between words. This composition allows us to extract long term dependencies in tweets and distinguish between offensive and non-offensive tweets. In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets. Our model achieved a macro F1-score of 79.40% on the SemEval dataset.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Valerio Basile
- Michael Granitzer
- Jelena Mitrović
- Preslav Nakov
- Sara Rosenthal
- Marcos Zampieri
- Tommaso Caselli
- Noura Farra
In Natural Language Processing
- Valerio Basile
- Michael Granitzer
- Jelena Mitrović
- Preslav Nakov
- Sara Rosenthal
- Marcos Zampieri
- Tommaso Caselli
- Ritesh Kumar
Citations in the corpus
- Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (2023, background): “…et al., 2018; van Aken et al., 2018; Gunasekara and Nejadgholi, 2018; Joulin et al., 2017; Kshirsagar et al., 2018; Mishra et al., 2018; Mitrovi´c et al., 2019; Sigurbergsson and Derczynski, 2020), contemporary approaches increasingly favor fine-tuning pre-trained models like Bert…”
- SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) (2019, background): “nlpUP (Mitrović et al., 2019) NULI (Liu et al.”
- HateBERT: Retraining BERT for Abusive Language Detection in English (2020, background): “…(Waseem and Hovy, 2016; Ribeiro et al., 2018), neural network architectures (e.g., CNN or Bi-LSTM) (Kshirsagar et al., 2018; Mishra et al., 2018; Mitrovi´c et al., 2019), and, finally, fine-tuning pre-trained language models, e.g., BERT, RoBERTa, among others (Liu et al., 2019; Swamy et al.,…”