In an example embodiment, factual question generation from freeform content is achieved through semantic role labeling and recurrent neural networks (RNNs). Specifically, semantic role labeling is used to identify an answer phrase so that it can be replaced with an appropriate question word. RNNs are then used to extract triples (Subject-Object-Predicate) from the sentence, and each of these triples can be used as an answer phrase/word. An RNN is then fed with training data to generate the questions more efficiently.