Temporal Question

Temporal question answering (TQA) focuses on developing systems that can accurately answer questions involving time-related constraints, a significant challenge in natural language processing. Current research emphasizes improving the ability of models, including those based on graph neural networks and large language models, to handle complex temporal reasoning, particularly across diverse data sources like knowledge graphs and semi-structured tables. This area is crucial for advancing knowledge base question answering and other applications requiring robust temporal understanding, ultimately leading to more sophisticated and accurate information retrieval systems.