Question Answering

Question Answering is an important task in the field of natural language processing, aimed at automatically answering questions posed by users through computer systems. This task can be subdivided into subtasks such as community question answering and knowledge base question answering, with evaluation metrics primarily including EM (Exact Match) and F1 scores. Currently, popular benchmark datasets include SQuAD, HotPotQA, bAbI, TriviaQA, and WikiQA. In recent years, models like T5 and XLNet have performed exceptionally well in this area, advancing the accuracy and practicality of question answering systems.