Paper:
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on. The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator. We also explore two approaches to accomplish the conditional discriminator: (1) phredGAN a , a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) phredGAN d , a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance. To demonstrate the superior performance of phredGAN over the persona SeqSeq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends. Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation. We also explore the trade-offs from using either variant of phredGAN on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).

Review:
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I have read the authors' response and other reviewers' comments carefully. Thank you for taking great efforts to improve the paper, including providing additional results on human evaluation. (Btw, Table 1 and Table 2 are also much nicer now.)
However, from the reviews it seems that all the reviewers agree that the novelty of this paper is limited, and the contribution is incremental.  I understand that this paper is the first and only work using adversarial framework for persona multi-turn conversation models. However, from the modeling perspective, I still think the novelty is limited.
As a summary, I have updated the score from 4 to 5 to reflect the efforts that the authors have been taken to improve the paper. However, due to reasons above, I still prefer a rejection recommendation. 
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Contributions:
The main contribution of this paper is the proposed phredGAN, which is a persona-based GAN framework for multi-turn dialogue modeling. Specifically, a persona-based HRED generator is developed, with two different kinds of discriminator design. Experiments are conducted on both the UDC and the TV series transcript datasets.  
Weaknesses:
(1) Novelty: I would say the novelty of this paper is rather limited. This paper heavily rely on the previous hredGAN work (Olabiyi et al., 2018), and extends it by injecting attributes into the system, borrowing ideas from the persona-based Seq2seq model (Li et al, 2016b). 
phredGAN_a is a straightforward extension of hredGAN, while phredGAN_d further introduces a collaborative discriminator that tries to predict the attribute that generated the input utterance. However, in summary, I think this paper is not novel enough. 
(2) Presentation: The paper is generally easy to follow and understand. However, I would say the paper is poorly written, and needs further polishing. For example, Table 1 & 2 are pretty ugly. 
(3) Evaluation: Generally, I think the experiments are not convincing and also not well-executed, with detailed comments listed below. 
Questions:
(1) In phredGAN_a, as shown in Eqn. (4), the attribute is used as input of the discriminator, while in phredGAN_d, as shown in Eqn. (5) & (6), the attribute is used as the target of the discriminator. My question is: why not use the attribute as both input & output? That is, why not combine (4) & (6), instead of using (5) & (6)? Please clarify this. 
(2) In experiments, Section 3.1, the authors mention that the generator and the discriminator use a shared encoder. However, the generator and discriminator has a different role. Since the encoder is shared, then: in one step, we update the encoder to minimize the GAN objective, in the alternative step, we update the encoder again to maximize the GAN objective. So, how to deal with this conflicting role of encoder during the training? Please clarify this. 
(3) From Table 2, it seems that it is difficult to see that phredGAN is better than hredGAN. Can you provide some explanations here?
(4) In Table 4, if the responses generated by hredGAN can be provided, that would be better to demonstrate the advantage of phredGAN. How does phredGAN compare with hredGAN qualitatively?
(5) From Table 1 & 2, it seems to me there is no metric that is specifically designed to evaluate whether the model captures the attribute information. Is there a way to quantitatively evaluate this? For example, pretrain an attribute classifier, or use the collaborative discriminator in the phredGAN_d model to measure how the generated response reflect the attribute. If we can observe the performance of phredGAN is better than that of hredGAN, that would be helpful for the paper.  
(6) Since the task is challenging, and the automatic metrics designed for this task is not perfect, like other papers, I think human evaluation is essential and desired for this task. However, such human evaluation is lacked in this paper.