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Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Detecting what emotions are expressed in text is a well-studied problem in natural language processing.
To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging
Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success.
Neural Word Decomposition Models for Abusive Language Detection
User generated text on social media often suffers from a lot of undesired characteristics including hatespeech, abusive language, insults etc.
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