RGTI:Response generation via templates integration for End to End dialog
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.
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)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
RGTI:Response generation via templates integration for End to End dialog
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



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)
assistant
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)