Embracing data abundance: BookTest Dataset for Reading Comprehension

Understand

There is a practically unlimited amount of natural language data available.

• Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities.
• This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger.
• We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture.