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20202026
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cs.CL20231 cited

Generative AI for Hate Speech Detection: Evaluation and Findings

Sagi Pendzel, Tomer Wullach, Amir Adler +1

Automatic hate speech detection using deep neural models is hampered by the scarcity of labeled datasets, leading to poor generalization. To mitigate this problem, generative AI ha…

cs.CL2023

Optimized Tokenization for Transcribed Error Correction

Tomer Wullach, Shlomo E. Chazan

The challenges facing speech recognition systems, such as variations in pronunciations, adverse audio conditions, and the scarcity of labeled data, emphasize the necessity for a po…

cs.CL2022

Enhancing Speech Recognition Decoding via Layer Aggregation

Tomer Wullach, Shlomo E. Chazan

Recently proposed speech recognition systems are designed to predict using representations generated by their top layers, employing greedy decoding which isolates each timestep fro…

cs.CL2021

Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech

Tomer Wullach, Amir Adler, Einat Minkov

Automatic hate speech detection is hampered by the scarcity of labeled datasetd, leading to poor generalization. We employ pretrained language models (LMs) to alleviate this data b…

cs.CL2020

Towards Hate Speech Detection at Large via Deep Generative Modeling

Tomer Wullach, Amir Adler, Einat Minkov

Hate speech detection is a critical problem in social media platforms, being often accused for enabling the spread of hatred and igniting physical violence. Hate speech detection r…