QuAC
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About
QuAC (Question Answering in Context) is a dataset designed for modeling and understanding information-seeking dialogues. It consists of interactive dialogs between two crowd workers where one poses freeform questions to learn about a hidden Wikipedia text, while the other provides short excerpts as answers. Unlike traditional datasets, QuAC includes open-ended and unanswerable questions that rely heavily on the context of the dialogue, introducing unique challenges. The dataset is intended for academic use and comes with significant limitations. It shares similarities with SQuAD 2.0 but incorporates a unique dialog component to enhance evaluation and model training. Users can download the dataset, access evaluation scripts, and submit models for scoring on the hidden test set. Official scores are generated based on submissions, ensuring the integrity of the results. For additional resources and guidance, users have the option to consult the QuAC paper, join discussions, and access baseline models available through AllenNLP.
Platform
Features
• interactive dialog dataset
• submission process for official scoring
• evaluation protocol similar to squad 2.0
• supports open-ended and unanswerable questions
• focus on information-seeking questions
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