Question Answering is a type of machine learning task that involves answering questions based on a given context. The task is typically performed on reading comprehension questions, where an AI system is trained to read a passage of text and answer questions related to that passage.

Types of Question Answering

Question answering can be segmented into various types, including domain-specific tasks like community question answering and knowledge-base question answering. In a community question answering system, users can post questions to a community of people who can then answer those questions. In contrast, a knowledge-base question answering system is designed to answer questions automatically based on a knowledge base.

Evaluation of Question Answering Systems

There are various benchmark datasets that are used to evaluate the performance of question answering systems, including SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. The metrics used to evaluate these models typically include EM (exact match) and F1, which assess the accuracy of the system's answers.

Recent Advancements in Question Answering

Recent advancements in NLP (natural language processing) techniques have led to improved performance in question answering systems. Some of the top-performing models include T5 and XLNet. These models use deep learning architectures to improve the accuracy and efficiency of question answering systems.

In summary, question answering is a type of machine learning task that involves answering questions based on a given context. There are various types of question answering systems that are used, including community question answering and knowledge-base question answering. The performance of these systems can be evaluated using benchmark datasets and metrics like EM and F1. Recent advancements in NLP techniques have led to improvements in the accuracy and efficiency of question answering systems.

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