Paper:
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.

Review:
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 $l_t$ 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?