Latent Topic Conversational Models
Despite much success in many large-scale language tasks, sequence-to-sequence (seq2seq) models have not been an ideal choice for conversational modeling as they tend to generate generic and repetitive responses. In this paper, we propose a Latent Topic Conversational Model (LTCM) that augments the seq2seq model with a neural topic component to better model human-human conversations. The neural topic component encodes information from the source sentence to build a global “topic” distribution over words, which is then consulted by the seq2seq model to improve generation at each time step. The experimental results show that the proposed LTCM can generate more diverse and interesting responses by sampling from its learnt latent representations. In a subjective human evaluation, the judges also confirm that LTCM is the preferred option comparing to competitive baseline models.
 The paper proposes a conversational model with topical information, by combining seq2seq model with neural topic models. The experiments and human evaluation show the model outperform some the baseline model seq2seq and the other latent variable model variant of seq2seq. The paper is interesting, but it also has certain limitations: 1) To my understanding, it is a straightforward combination of seq2seq and one of the neural topic models without any justification. 2) The evaluation doesn*t show how the topic information could influence word generation. No of the metrics in table 2 could be used to justify the effect of topical information. 3) There is no analysis about the model behavior, therefore there is no way we could get a sense about how the model actually works. One possible analysis is to investigate the values and the corresponding words, which to some extent will tell us how the topical information be used in generation. In addition, it could be even better if there are some analysis about topics extracted by this model. This paper also doesn*t pay much attention to the existing work on topic-driven conversational modeling. For example "Topic Aware Neural Response Generation" from Xing et al., 2017. Some additional issues: 1) In the second line under equation 4, y_{t-1} -> y_{t} 2) In the first paragraph of section 3, two "MLP"*s are confusing 3) In the first paragraph of page 6, words with "highest inverse document frequency" are used as stop words?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Latent Topic Conversational Models
Despite much success in many large-scale language tasks, sequence-to-sequence (seq2seq) models have not been an ideal choice for conversational modeling as they tend to generate generic and repetitive responses. In this paper, we propose a Latent Topic Conversational Model (LTCM) that augments the seq2seq model with a neural topic component to better model human-human conversations. The neural topic component encodes information from the source sentence to build a global “topic” distribution over words, which is then consulted by the seq2seq model to improve generation at each time step. The experimental results show that the proposed LTCM can generate more diverse and interesting responses by sampling from its learnt latent representations. In a subjective human evaluation, the judges also confirm that LTCM is the preferred option comparing to competitive baseline models.
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>



 The paper proposes a conversational model with topical information, by combining seq2seq model with neural topic models. The experiments and human evaluation show the model outperform some the baseline model seq2seq and the other latent variable model variant of seq2seq. The paper is interesting, but it also has certain limitations: 1) To my understanding, it is a straightforward combination of seq2seq and one of the neural topic models without any justification. 2) The evaluation doesn*t show how the topic information could influence word generation. No of the metrics in table 2 could be used to justify the effect of topical information. 3) There is no analysis about the model behavior, therefore there is no way we could get a sense about how the model actually works. One possible analysis is to investigate the values and the corresponding words, which to some extent will tell us how the topical information be used in generation. In addition, it could be even better if there are some analysis about topics extracted by this model. This paper also doesn*t pay much attention to the existing work on topic-driven conversational modeling. For example "Topic Aware Neural Response Generation" from Xing et al., 2017. Some additional issues: 1) In the second line under equation 4, y_{t-1} -> y_{t} 2) In the first paragraph of section 3, two "MLP"*s are confusing 3) In the first paragraph of page 6, words with "highest inverse document frequency" are used as stop words?
assistant
 The paper proposes a conversational model with topical information, by combining seq2seq model with neural topic models. The experiments and human evaluation show the model outperform some the baseline model seq2seq and the other latent variable model variant of seq2seq. The paper is interesting, but it also has certain limitations: 1) To my understanding, it is a straightforward combination of seq2seq and one of the neural topic models without any justification. 2) The evaluation doesn*t show how the topic information could influence word generation. No of the metrics in table 2 could be used to justify the effect of topical information. 3) There is no analysis about the model behavior, therefore there is no way we could get a sense about how the model actually works. One possible analysis is to investigate the values and the corresponding words, which to some extent will tell us how the topical information be used in generation. In addition, it could be even better if there are some analysis about topics extracted by this model. This paper also doesn*t pay much attention to the existing work on topic-driven conversational modeling. For example "Topic Aware Neural Response Generation" from Xing et al., 2017. Some additional issues: 1) In the second line under equation 4, y_{t-1} -> y_{t} 2) In the first paragraph of section 3, two "MLP"*s are confusing 3) In the first paragraph of page 6, words with "highest inverse document frequency" are used as stop words?