Google AI researchers find strange new reason to play Jeopardy! Posted on : Oct 13 - 2018

When IBM's Watson computer beat two world champions at the game show Jeopardy! in 2011, it was a moment to marvel at how a machine could take comprehend the language of a question and could mine its vast memory for an appropriate response.

Google scientists have found another use for Jeopardy! questions, having little to do with understanding human speech and more about how computers communicate with one another.

And this week, they've made that work an open-source software tool available on GitHub to anyone using Google's TensorFlow framework for machine learning.

"Active Question Answering," or Active QA, as the TensorFlow package is called, will reformulate a given English-language question into multiple different re-wordings, and find the variant that does best at retrieving an answer from a database.

The system was developed by feeding Jeopardy! clues into a "reinforcement learning" neural network. The network got better and better at re-wording questions as it was rewarded for successfully retrieving the right answer.

Google AI authors, in the blog post on the project, note that their famous corporate mission is to "organize the world's information." In keeping with that, they "envision that this research will help us design systems that provide better and more interpretable answers, and hope it will help others develop systems that can interact with the world using natural language."

In the original paper, Ask The Right Questions: Active Question Reformulation With Reinforcement Learning, presented this past spring at the International Conference on Learning Representations, Google AI researchers Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Wojciech Gajewski, Andrea Gesmundo, Neil Houlsby, and Wei Wang built upon principles of machine translation. They interpreted the task of training a computer to reformulate clues from Jeopardy! as being akin to foreign language translation. The goal was to paraphrase the Jeopardy! clues in a syntax that improves querying of a database.

For example, given a clue like "Gandhi was deeply influenced by this count who wrote 'War and Peace'," the neural network had to learn to put that clue into the form of a question that would produce the correct answer, which is Leo Tolstoy. (The Jeopardy! questions were gotten from a 2017 project, called SearchQA, built by researchers at New York University and Carnegie Mellon. Their project was, in turn, taken by crawling the ebsite "J! Archive," a fan site for the show.)

The Active QA package includes the a customized version of Google's TensorFlow code for machine translation. It's based on Google research in 2014 on what's called "sequence to sequence" neural networks for translating between, say, English and French. View More