Paper:
End-to-end models have achieved considerable success in task-oriented dialogue area, but suffer from the challenges of (a) poor semantic control, and (b) little interaction with auxiliary information. In this paper, we propose a novel yet simple end-to-end model for response generation via mixed templates, which can address above challenges. In our model, we retrieval candidate responses which contain abundant syntactic and sequence information by dialogue semantic information related to dialogue history. Then, we exploit candidate response attention to get templates which should be mentioned in response. Our model can integrate multi template information to guide the decoder module how to generate response better. We show that our proposed model learns useful templates information, which improves the performance of "how to say" and "what to say" in response generation. Experiments on the large-scale Multiwoz dataset demonstrate the effectiveness of our proposed model, which attain the state-of-the-art performance.

Review:
This paper describes a method to incorporate candidate templates to aid in response generation within an end-to-end dialog system. While the motivation and task setup is interesting, the paper is clearly unfinished. Most jarringly, Table 2 which should contain the main results comparing the proposed RGTI model to existing baseline models is not filled in, and there appears to be no table showing results of the ablation study briefly described in Section 3.4.
Other issues include the lack of detail around any of the subtasks mention. For example, in Sec 2.1, how is semantic information used as search criteria to retrieve responses? We also need to see more details about where responses are retrieved from. In Sec 2.2, how is the splitting done to construct tuples from the candidate templates (the word "in" is dropped in the example given, and it's not clear if this is a typo or whether some processing is done over the phrases before tuple construction is done).
Other comments:
- The paper needs to be heavily proofread. There are many spelling/grammar mistakes, for example:
	- dilogue -> dialogue (Figure 2, page 2)
	- acurrate -> accurate (top of page 3)
        - "First we should retrieval responses" -> "retrieve responses" (page 3)
        - BlEU -> BLEU (page 5)