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VASIC, or Virtual Assistant Semantic Interpretation Corpus, is a dataset created by researchers at the University of Washington. It is designed to facilitate the training and evaluation of virtual assistants, specifically focusing on semantic interpretation tasks.The dataset consists of more than 25,000 annotated utterances collected from real-world interactions with virtual assistants. These utterances cover a wide range of topics and include various types of user inputs, such as commands, queries, and requests. Each utterance is labeled with semantic frames that capture the meaning and intention behind the user's input.Semantic frames in VASIC are represented using a hierarchical structure consisting of slots and intents. Slots represent specific entities or attributes mentioned by the user, while intents capture the overall intention or action desired by the user. For example, in the sentence "What's the weather like in Seattle?", the slot would be "Seattle" (representing the location) and the intent would be "weather" (representing the desired information).The VASIC dataset has been used to train and evaluate various natural language understanding models for virtual assistants. Its rich annotation scheme allows for fine-grained analysis of semantic interpretation performance and can help improve the accuracy and effectiveness of virtual assistant systems.Overall, VASIC provides a valuable resource for researchers working on natural language understanding and dialogue systems, enabling them to develop more advanced virtual assistant technologies.

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