Teaching Computers to Answer Simple Questions — but at Big Scale
Imagine asking a computer a plain question and it finds the right fact quickly, even when the answers hide in millions of lines of text.
That's the challenge: simple questions, but huge piles of data make finding the right evidence hard.
To tackle this, researchers created a new set of 100,000 questions to train systems, so they see many kinds of queries and learn faster, even when some questions look different than before.
The study shows that a method called Memory Networks can learn to pull the right facts out, and that using multitask and transfer learning helps the system use what it learned in one place to answer elsewhere.
The result is better answers across big collections, and it's a step toward helpers that can search smarter for your question.
There is more to fix and improve, but this work proves simple reasoning can scale, and it is hopeful for tools we use everyday.
Read article comprehensive review in Paperium.net:
Large-scale Simple Question Answering with Memory Networks
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