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Approaches to Counterspeech Detection and Generation Using NLP Techniques
- Approaches to counterspeech detection have focused on binary classification and multi-label tasks, analyzing abuse and counterspeech in various contexts.
- Counterspeech detection involves evaluating pre-trained language models for categorizing strategies across different languages.
- Counterspeech generation methods center on transformer-based models, emphasizing aspects like efficacy, informativeness, multilinguality, politeness, and diversity.
- Testing multiple decoding mechanisms, autoregressive models with stochastic decoding are found to offer optimal counterspeech generation.
- Evaluation of counterspeech generation poses challenges due to the lack of clear criteria, relying on both automatic metrics and human evaluation.
- Automatic metrics evaluate generation quality using linguistic criteria, novelty, and repetitiveness, while human evaluation focuses on aspects like suitableness and grammatical accuracy.
- Counterspeech generation is seen as a newer research area that can complement content moderation efforts in addressing hate speech.
- Challenges in counterspeech generation include ensuring faithfulness, avoiding toxic degeneration, and striking a balance between generalisation and specialisation.
- Issues of data bias, unintended toxicity, and generalisability impact the effectiveness and ethical considerations of automated counterspeech generation.
- Deploying counterspeech generation as suggestion tools alongside human moderation is advocated to assist in countering hate speech effectively.
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