For our course project, we aim to reverse the roles of the computer and human, such that the computer will act as an answerer and a human as a questioner. In the past, no such study has been conducted as this problem presented sophisticated challenges of Natural Language Inference and Textual Entailment. However, with the advent of transformer-based machine learning techniques such as BERT [1], RoBERTa [2], GPT-2 [3], and datasets such as BoolQ [4], such a model can be constructed. As this problem has not been formally defined, our goal is to formalize it and present preliminary results regarding the same. Furthermore, while there are several pre-trained question-answering models that select the start and end points of a corpus containing an answer, a simple yes/no answering task is surprisingly challenging and complex. A model for such a task would have to examine entailment as well as investigate if the corpus makes a positive answer to the question unlikely, even if it doesn't directly state a negative answer [4]. Our reverse Akinator model could be used for any sort of factual checker to examine whether a statement is true or not, given a knowledge corpus.

This post is based on Most Viewed Writers in Machine Learning, the 10 writers with the most answer views in the last 30 days, as retrieved on June 25, 2017. Just so there is no confusion, please note that this post is "authored" by me, but none of the information contained herein -- from the questions to the answers -- has anything to do with me. Excerpt from answer to: To what extent does Håkon Hapnes Strand feel that it's important to memorise advanced formulae in machine learning algorithms? I don't think it's important to memorize formulae. In fact, I think it can even be counter-productive.