Asking questions is an important ability for a chatbot. This paper focuses on question generation. Although there are existing works on question generation based on a piece of descriptive text, it remains to be a very challenging problem. In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text. The key reason for proposing the new problem is that in practical applications, we found that useful questions need to be targeted toward some relevant topics. One almost never asks a random question in a conversation. Due to the fact that given a descriptive text, it is often possible to ask many types of questions, generating a question without knowing what it is about is of limited use. To solve the problem, we propose a novel neural network that is able to generate topic-specific questions. One major advantage of this model is that it can be trained directly using a question-answering corpus without requiring any additional annotations like annotating topics in the questions or answers. Experimental results show that our model outperforms the state-of-the-art baseline.
This paper presents a neural network-based approach to generate topic-specific questions with the motivation that topical questions are more meaningful in practical applications like real-world conversations. Experiments and evaluation have been conducted on the AQAD corpus to show the effectiveness of the approach.
Although the main contributions are clear, the paper contains numerous typos, grammatical errors, incomplete sentences, and a lot of discrepancies between text, notations, and figures making it ambiguous and difficult to follow. 
Authors claim to generate topic-specific questions, however, the dataset choice, experiments, and examples show that the generated questions are essentially keyword/key phrase-based. This is also apparent in Section 4.1 where authors present some observation without any supporting proof or empirical evidence. Moreover, the example in Figure 1 shows a conversation, but, typically, in an ongoing multi-round conversation people do not tend to repeat the keywords or key phrases or named entities, and topic shifts might occur at any time. 
Overall, a misconception about topic vs. keywords might have led the authors to claim that their work is the first to generate topic-specific questions whereas this has been studied before by Chali & Hasan (2015) in a non-neural setting. "Topic" in general has a broader meaning, I would suggest authors to see this to get an idea about what topic entails to in a conversational setting: https://developer.amazon.com/alexaprize/contest-rules . I think the proposed work is mostly related to: 1) "Towards Natural Question-Guided Search" by Kotov and Zhai (2010), and 2) "K2Q: Generating Natural Language Questions from Keywords with User Refinements" by Zheng et al. (2011), and other recent factoid question generation papers where questions are generated from a given fact (e.g. "Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus" by Serban et al. (2016)).
It is not clear how the question types are extracted from the given sentences. Please provide details. Which keywords are employed to accomplish this? Also, please explain the absence of the "why" type question. 
Figure 3 and the associated descriptions are very hard to follow. Please draw the figure by matching it with the descriptions.  Where are the bi-LSTMs in the figure? What are ac_t and em_t? 
My major concern is with the experiments and evaluation. The dataset essentially contains questions about product reviews and does not match authors motivation/observation about real-world conversations. Moreover, evaluation has been conducted on a very small test set (just about 1% of the selected corpus), making the results unconvincing. More details are necessary about how exactly Kim's and Liu's models are used to get question types and topics. 
Human evaluation results per category would have been more useful. How did you combine the scores of the human evaluation categories? Also, automatic evaluation and human evaluation results do not correlate well. Please explain.