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I wanted to train a chatbot for answering questions from books. I am trying to use Dynamic Memory Networks to do so.

How can I generate a data set, as Facebook did in the case of bAbI tasks, so that it can tackle a variety of questions on the data set?

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If you are talking about "generating" in the sense of generative models , it is pretty tough. since we are still far beyond understanding the actual structure of question-answering.

And even state of the art methods for question answering are also not able to score well on datasets like babi , mostly 16 out of 20 tasks can be solved.

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  • $\begingroup$ Thanks a lot for answering @thecomplexitytheorist. I wanted to create BaBI like tasks from a custom dataset - like a book. So it was not exactly like fully generating from scratch. $\endgroup$
    – Shubham Bhardwaj
    Commented Nov 2, 2018 at 8:15
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This repository maintained by Facebook AI Research talks about how they went about generating QA from stories.

In essence, they try to simulate how a reader reads a story. They also keep track about the knowledge the reader is assimilating when reading. Then they frame a question based on the knowledge assimilated, in order to asses if the reader can perform logical inference given what knowledge is present with him.

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