Lifelong Learning of Hate Speech Classification on Social Media
By Jing Qian, Hong Wang, Mai Elsherief, Xifeng Yan. 2021
27 citations recorded by the source corpus.
Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. However, the amount of data in social media increases every day, and the hot topics changes rapidly, requiring the classifiers to be able to continuously adapt to new data without forgetting the previously learned knowledge. This ability, referred to as lifelong learning, is crucial for the real-word application of hate speech classifiers in social media. In this work, we propose lifelong learning of hate speech classification on social media. To alleviate catastrophic forgetting, we propose to use Variational Representation Learning (VRL) along with a memory module based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural Network). Experimentally, we show that combining variational representation learning and the LB-SOINN memory module achieves better performance than the commonly-used lifelong learning techniques.
Subfields: Artificial Intelligence, Computer Networks and Communications
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In Artificial Intelligence
- Diyi Yang
- David Muchlinski
- Caleb Ziems
- Gautam M. Shroff
- Tanmoy Chakraborty
- Vaibhav Varshney
- Tanmay Garg
- Tharun Suresh
In Computer Networks and Communications
Citations in the corpus
- Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection (2022, methodology): “…“ P rompt A ugmented Ge nerative R eplay” ( PAGeR ) model where, similar to existing approaches (de Masson d'Autume et al., 2019; Wang et al., 2020; Qian et al., 2021; Xia et al., 2021; Wang et al., 2019; Obamuyide and Vlachos, 2019; Han et al., 2020) we do not store real samples in replay memory…”
- Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021, background): “…future work could explore deciphering models for coded language (Kambhatla et al., 2018; Qian et al., 2019), lifelong learning of hateful language (Qian et al., 2021), contextualized sarcasm detection, and bias mitigation for named entities in hate speech detection systems (Xia et al., 2020) and…”
- Handling Bias in Toxic Speech Detection: A Survey (2022, background): “[76] proposed a prototypical learned model for hate speech classiication that aims to capture the evolving hateful content as it develops.”