Incremental Sequence

Incremental sequence labeling focuses on building models that process sequential data (like text or speech) piece-by-piece, updating predictions as new information arrives. Current research emphasizes improving model robustness by mitigating issues like "catastrophic forgetting" (losing knowledge of previously learned patterns) and developing effective revision policies to correct errors in real-time. This involves exploring various architectures, including Transformer-based models and Hidden CRFs, often leveraging pre-trained language models to enhance performance and reduce reliance on large labeled datasets. The resulting advancements have implications for various applications, including improved natural language processing and speech recognition systems.