QuAC
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About
QuAC (Question Answering in Context) is a dataset specifically created for modeling, understanding, and engaging in information-seeking dialogues. It consists of interactive conversations between a student, who asks a series of freeform questions to learn about a hidden Wikipedia text, and a teacher, who provides short excerpts (spans) from the text as answers. Unlike existing machine comprehension datasets, QuAC presents unique challenges because its questions are often open-ended, may be unanswerable, or are only meaningful within the context of the ongoing dialogue. It shares principles with SQuAD 2.0, such as span-based evaluation and unanswerable questions, but adds a crucial dialog component. QuAC is intended as an academic resource and comes with a detailed datasheet outlining its limitations for practical applications. Researchers can download training and validation sets, use an evaluation script, and submit models for official scoring on a hidden test set via a submission tutorial.
Platform
Features
• baseline models via allennlp
• evaluation script & submission system
• downloadable training and validation sets
• squad 2.0 similar evaluation
• context-dependent and unanswerable questions
• span-based answers from text
• interactive student-teacher q&a
• dataset for information-seeking dialog
FAQs
What is QuAC?
QuAC is a dataset for modeling, understanding, and participating in information seeking dialog, featuring interactive Q&A between a student and a teacher based on a hidden Wikipedia text.
Is QuAC exactly like SQuAD 2.0?
No, QuAC shares principles like span-based evaluation and unanswerable questions with SQuAD 2.0 but uniquely incorporates a new dialog component for information-seeking conversations.
How do I get the duck in my paper?
Download the daffyhand.pdf duck image and use the LaTeX macro \newcommand{\daffy}[0]{\includegraphics[width=.04\textwidth]{path_to_daffy/daffyhand.pdf}} to include it.
Pricing Plans
Free
Free Plan• Downloadable Training Set
• Downloadable Validation Set
• Evaluation Script
• Sample Prediction File
• Submission to Leaderboard
• Baseline Models via AllenNLP
Job Opportunities
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