RGTI:Response generation via templates integration for End to End dialog

Abstract: 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.

Reviews and author responses:

1. Summary: The authors proposed a deep neural network-based model to generate responses fro task-oriented dialogue systems. The model mainly contains two parts, the first part is to retrieve relevant responses based on question and encode them into templates, the second part is a decoder to generate the response based on the encoded templates and input utterances.
2. Assessment: The problem studied in this work is very interesting and important. The high-level idea also looks interesting. However, this paper is clearly not ready to submit as it contains a lot of grammar errors and lacks a lot of important details. 
2.1 "In our model, we retrieval ..." => "we retrieve", "which is composed of a encoder ..." => "an encoder". There are too many such language errors in this paper. 
2.2 Table 2 is not finished yet. There's only one result in it.
2.3 What does "triple form" mean? What are the candidate responses to retrieve when doing inference? What are the details of reference response retrieval? To many such important details are missing.

The basic idea of integrating templates for dialog generation is interesting. However, the implementation is confusing, yet another architecture, with clear motivations, intuitions, or even clarity. Worst of all, the experimental results are missing in table 2, the only placeholder for results in the paper. Further, I would appreciate human evaluation, rather than using BLEU as the metric. The latter is known to be inappropriate for dialog modeling evaluation.

Section 2.1 needs more explanation.

(Strong Reject)

This paper introduces a based model for the problem of task-oriented dialog. The main novelty seems to be: 1) the model can mix multiple templates to respond; 2) the response is generated using a combination of templates and a language model (directly) conditioned on the input.

The paper studies an interesting task however it is not ready for
publication:
- Empirical results of all baselines are missing (Table 2)
- Other results are mentioned but are absent from the manuscript
- The related work does not provide details as to how the proposed model to prior work
- The figures are difficult to understand (although they do help make the model clearer and so I suggest polishing them for the next version of this work)
- The manuscript should be carefully copy-edited.


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)

Meta-review: This paper describes a method to incorporate multiple candidate templates to aid in response generation for an end-to-end dialog system. Reviewers thought the basic idea is novel and interesting. However, they also agree that the paper is far from complete, results are missing, further experiments are needed as justification, and the presentation of the paper is not very clear. Given the these feedback from the reviews, I suggest rejecting the paper.

Decision: reject