Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish
By Renard Korzeniowski, Rafal Rolczynski, Przemyslaw Sadownik, Tomasz Korbak, Marcin Możejko, Marcin Mozejko. 2019
4 citations recorded by the source corpus.
This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection. We describe three parallel approaches that we followed: fine-tuning a pre-trained ULMFiT model to our classification task, fine-tuning a pre-trained BERT model to our classification task, and using the TPOT library to find the optimal pipeline. We present results achieved by these three tools and review their advantages and disadvantages in terms of user experience. Our team placed second in subtask 2 with a shallow model found by TPOT: a~logistic regression classifier with non-trivial feature engineering.
Subfields: Artificial Intelligence, Natural Language Processing, Efficiency & Compression
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
- Yulia Tsvetkov
- Vidhisha Balachandran
- Sachin Kumar
- Antonios Anastasopoulos
- Lucille Njoo
- Mohit Bansal
- Anjalie Field
- Hinrich Schütze
In Natural Language Processing
- Yulia Tsvetkov
- Sachin Kumar
- Vidhisha Balachandran
- Antonios Anastasopoulos
- Lucille Njoo
- Anjalie Field
- Hinrich Schütze
- Mohit Bansal
In Efficiency & Compression
Citations in the corpus
- Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, methodology): “Hence, unsupervised or distantly supervised approaches have been adopted to detect it (Korzeniowski et al., 2019; Field and Tsvetkov, 2020a; Ògúnremí et al., 2021).”