Trigger Free Event Detection

Trigger-free event detection aims to identify and classify events in text or other data streams without relying on explicitly identifying "trigger" words, thus reducing annotation burden and improving robustness. Current research focuses on adapting large language models and machine reading comprehension techniques, often employing prompt learning and novel architectures like two-tower models or sparse submanifold convolutional neural networks for efficiency and accuracy, particularly in low-resource scenarios. This approach holds significant promise for advancing various fields, including natural language processing, robotics (e.g., improving visual place recognition and efficient object tracking), and scientific data analysis (e.g., neutrino telescope event reconstruction), by enabling more efficient and scalable event processing.