NarrativeQA
A reading comprehension dataset with Wikipedia summaries, full stories, and question-answer pairs for narrative understanding.
What is NarrativeQA?
NarrativeQA is a reading comprehension dataset created by DeepMind for evaluating machine understanding of entire narratives. It consists of documents with Wikipedia summaries, links to full stories (books and movie scripts), and corresponding question-answer pairs. The dataset challenges models to comprehend long-form text rather than just extract factual information from short passages.
Researchers and developers working on natural language processing, particularly in reading comprehension, question answering, and narrative understanding tasks. It's especially relevant for those building or evaluating models that need to understand long documents and complex storylines.
Unlike many QA datasets that focus on factoid extraction from short passages, NarrativeQA requires understanding of entire narratives, making it more challenging and realistic. It provides a benchmark for testing true reading comprehension capabilities in AI systems.
Overview
This repository contains the NarrativeQA dataset. It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers.
Use Cases
Best For
- Training reading comprehension models on long-form narratives
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