Question Answering

Question Answering is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context. Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include [SQuAD](/dataset/squad), [HotPotQA](/dataset/hotpotqa), [bAbI](/dataset/babi-1), [TriviaQA](/dataset/triviaqa), [WikiQA](/dataset/wikiqa), and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet. Source: [SQuAD](https://rajpurkar.github.io/mlx/qa-and-squad/) )

相关学科： BERTKnowledge GraphsNERMachine TranslationPassage RetrievalReading ComprehensionAnswer SelectionText SummarizationRelation ExtractionOpen-Domain Question Answering

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