QuAC
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About
QuAC (Question Answering in Context) is a dataset designed for research in question answering within a conversational context. It features a dialog between a student asking questions about a hidden Wikipedia text and a teacher providing answers as excerpts from the text. Unlike existing datasets, QuAC's questions are often open-ended, unanswerable, or context-dependent, making it challenging but valuable for advancing dialogue-based question answering models. The dataset is distributed under the CC BY-SA 4.0 license, and includes training and validation sets, along with an evaluation script and sample prediction file. A leaderboard showcases various models' performance on the dataset.
Platform
Features
• question answering
• dataset
• dialog modeling
FAQs
Is QuAC exactly like SQuAD 2.0?
No, QuAC shares many principles with SQuAD 2.0 such as span based evaluation and unanswerable questions, but incorporates a new dialog component.
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