Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks. The recent success of large pre-trained language models such as BERT and GPT-2 (Devlin et al., 2019;Radford et al., 2019) have suggested the effectiveness of incorporating language priors in down-stream NLP tasks. However, how much pre-trained language models can help dialog response generation is still under exploration. In this paper, we propose a simple, general, and effective framework: Alternating Roles Dialog Model (ARDM). ARDM models each speaker separately and takes advantage of the large pretrained language model. It requires no supervision from human annotations such as belief states or dialog acts to achieve effective conversations. ARDM outperforms or is on par with state-of-the-art methods on two popular task-oriented dialog datasets: CamRest676 and MultiWOZ. Moreover, we can generalize ARDM to more challenging, non-collaborative tasks such as persuasion. In persuasion tasks, ARDM is capable of generating human-like responses to persuade people to donate to a charity.
The paper is concerned with reducing the amount of manual dialog state or dialog act labeling in task-oriented dialog, following the lead of Eric and Manning (2017) who did not require any such explicit annotation. The authors note that Eric and Manning’s approach didn’t do well on more recent dialog datasets in comparison to e.g. Sequicity (Lei et al., 2018), which trains an explicit belief state. The first main contribution of the submission is to exploit GPT-2 to outperform Sequicitiy and other techniques, doing so without explicit dialog state/act supervision. The second contribution is an alternating parameterization of the model that distinguishes between agent and user utterances. Experimental results show that the method of the paper (ARDM) is either superior or on par with methods that exploit hand-labeled dialog states/acts.
Originality/impact:
I think the approach of the paper is well motivated, though quite simple. The idea of improving on a generation task with GPT-2 is a tried one, and its effectiveness isn’t really surprising. The alternating parameterization is interesting but has been done before in (e.g.) Zhao and Kawahara (2019) and (Zhang et al., 2019) [1]. Overall, I think the paper doesn’t bring much novelty, but results are promising, and this work represents a step towards making task-oriented dialog system less reliant and hand coded linguistic information. It could have an impact for the dialog community.
Empirical contribution:
My main concern with the paper is a lack of ablation, which makes it hard to understand where the improvement comes from. More specifically:
* It is not known how much the alternating nature of ARDM contributes the overall gains compared to (1) other details of the model; (2) pre-training with GPT-2. One way to ablate ARDM’s alternating mechanism from other characteristics of the model would be for example to tie the parameters of the user and the agent. 
* Table 1 compares GPT-2 with ARMD, so we don’t know if the improvement comes from either fine tuning on CamRest676, from ARDM’s alternation between speakers, or from other characteristics of ARDM. Note: The text characterizes GPT-2 as “a pre-trained large-scale language model GPT-2”, so I presume “GPT-2” here means without fine-tuning.
* Results of Table 3 are perplexing and more analyzes would be needed to understand what is really happening. Considering that both TransferTransfo and ARDM are based on transformer and GPT-2, and are apparently fine-tuned on the same data, I find it strange the gap between the two is so big on perplexity (10.1 vs TransferTransfo’s 19.9) while TransferTransfo is actually superior on BLEU. I understand BLEU is problematic for dialog evaluation as it treats references (gold responses) as the only reasonable responses (Liu et al., 2016), but that makes perplexity as an evaluation metric problematic for exactly the same reason. In fact, (Schwenk et al., 2006)[2], (Luong et al., 2016)[3], and others have shown that BLEU and perplexity are highly correlated, which isn’t surprising as both penalize generated responses to the extent that they lexically differ from the gold standard. So, these results look a bit strange/suspicious to me, and I think deserve further investigation. Sample outputs of both TransferTransfo and ARDM might help better understand the problem (Why showing only ARDM’s? When showing generation examples, I think it is standard practice to show outputs of multiple systems, and not just for the system of the paper). 
Minor comments:
- [Manning and Eric, 2017] should be [Eric and Manning, 2017]
- ARDM with “recurrent” maybe isn’t a good name as it may imply it is RNN instead of Transformer based.
- Section 3.1: I suggest you explain the “memory mechanism” more formally and in more details, e.g., M_{t-1} doesn’t seem to be used anywhere.
- “assume there is only one layer in Transformer”: Is it for the sake of the presentation only, I assume?
- Tables 1-3: perhaps add confidence intervals as some of the differences are quite small, and so are some of the test sets.
- Table 3 doesn’t seem to be referenced anywhere.
- Table 4: I would recommend showing ARDM and TransferTransfo side by side.
[1]: https://arxiv.org/abs/1903.05759
[2]: https://www.aclweb.org/anthology/P06-2093/ (e.g., Fig. 5 shows the high correlation between BLEU and perplexity)
[3]: https://arxiv.org/abs/1410.8206
Questions:
(1) In 4.3.1, are capacities of the two models the same? If ARDM has (roughly) double the number of parameters due to its modeling of two speakers, what about comparing TransferTransfo at same model capacity to make the comparison more meaningful?
(2) I am confused how you (the authors) compute belief state success F1 for GPT-2, as the text says there is only pre-training with this model, which I think implies there is no fine-tuning on the belief state identification task. Could you provide more details?