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Found 7 papers, 2 papers with code
Generative AI for Hate Speech Detection: Evaluation and Findings
In addition, we explore and compare the performance of the finetuned LLMs with zero-shot hate detection using a GPT-3. 5 model.
Optimized Tokenization for Transcribed Error Correction
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 post-processing step that corrects recurring errors.
Don't Be So Sure! Boosting ASR Decoding via Confidence Relaxation
One prominent speech recognition decoding heuristic is beam search, which seeks the transcript with the greatest likelihood computed using the predicted distribution.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Enhancing Speech Recognition Decoding via Layer Aggregation
Recently proposed speech recognition systems are designed to predict using representations generated by their top layers, employing greedy decoding which isolates each timestep from the rest of the sequence.
Character-level HyperNetworks for Hate Speech Detection
Our results show that the proposed HyperNetworks achieve performance that is competitive, and better in some cases, than these pretrained language models, while being smaller by orders of magnitude.
Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech
Automatic hate speech detection is hampered by the scarcity of labeled datasetd, leading to poor generalization.
Towards Hate Speech Detection at Large via Deep Generative Modeling
Hate speech detection is a critical problem in social media platforms, being often accused for enabling the spread of hatred and igniting physical violence.
