Sequence Tagging Model

Sequence tagging models are machine learning techniques used to assign labels to individual elements within a sequence of data, such as words in a sentence or frames in an audio clip. Current research focuses on improving model performance and robustness across diverse applications, leveraging architectures like transformers and incorporating techniques like data augmentation and adversarial training to address challenges in low-resource settings and noisy data. These advancements are driving progress in various fields, including natural language processing (e.g., grammatical error correction, named entity recognition), audio analysis (e.g., deepfake detection), and knowledge extraction (e.g., identifying causal relationships). The resulting improvements in accuracy and efficiency have significant implications for numerous applications requiring automated text and audio processing.