text: The authors propose a scheme to generate questions based on some answer sentences, topics and question types.  Topics are extracted from questions using similar words in question-answer pairs.  It is similar to what we find in some Q&A systems (like lexical answer types in Watson).  A sequence classifier is also used to tag the presence of topic words.  Question types correspond mostly to salient questions words.  LSTMs are used to encode the various inputs and generate the questions.  The paper is well written and easy to follow.  I would expect more explanations why sentence classification and labeling results presented in Table 2 are so low.  Experimental results on question generation are convincing and clearly indicate that the approach is effective to generate relevant and well-structured short questions.  The main weakness of the paper is the selected set of question types that seems to be a fuzzy combination of answer types and question types (for ex. yes/no).  Some questions type can be highly ambiguous;  for instance “What” might lead to a definition, a quantity, some named entities...  Hence I suggest you revise your qt set.  I would also suggest, for your next experiments, that you try to generate questions leading to answers with list of values.