Sequential Annotation

Sequential annotation focuses on labeling ordered data, such as steps in a procedure or interactions in a conversation, to enable machine learning models to understand temporal dependencies and complex sequences of events. Current research emphasizes developing efficient annotation methods, particularly for handling inconsistencies across annotators and exploring alternative representations like flow graphs to reduce annotation burden. This work is crucial for improving the accuracy and robustness of AI systems in various applications, including human-robot interaction, procedural understanding, and natural language processing, by providing high-quality training data for sequence labeling tasks.