Question Answering in Context
Question Answering in Context (QuAC) is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context.
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Tracking Progress in Natural Language Processing | NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Visualqa.org
VQA: Visual Question Answering
What is VQA? VQA is a new dataset containing open-ended questions about images. These questions require an understanding of vision, language and commonsense knowledge to answer. 265,016 images (COCO and abstract scenes) At least 3 questions (5.4 questions on average) per image 10 ground truth answers per question 3 plausible...
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HQuack — Live Answers to HQ Trivia!
Welcome to HQuack — the website that predicts HQ Trivia answers LIVE with up to 82% accuracy. Check back in before next game to experience the magic yourself.
Parl.ai
ParlAI
Examples Display 10 random examples from task 1 of the "1k training examples" bAbI task: Run this command: parlai display_data --task babi:task1k:1 Displays 100 random examples from multitasking on the bAbI task and the SQuAD dataset at the same time: Run this command: parlai display_data --task babi:task1k:1,squad -n 100 Evaluate...
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