Paper:
We study non-collaborative dialogs, where two agents have a conflict of interest but must strategically communicate to reach an agreement (e.g., negotiation). This setting poses new challenges for modeling dialog history because the dialog's outcome relies not only on the semantic intent, but also on tactics that convey the intent. We propose to model both semantic and tactic history using finite state transducers (FSTs). Unlike RNN, FSTs can explicitly represent dialog history through all the states traversed, facilitating interpretability of dialog structure. We train FSTs on a set of strategies and tactics used in negotiation dialogs. The trained FSTs show plausible tactic structure and can be generalized to other noncollaborative domains (e.g., persuasion). We evaluate the FSTs by incorporating them in an automated negotiating system that attempts to sell products and a persuasion system that persuades people to donate to a charity. Experiments show that explicitly modeling both semantic and tactic history is an effective way to improve both dialog policy planning and generation performance.

Review:
This paper proposed a framework for non-collaborative dialog systems. To track the history better, the authors propose to apply two pre-trained finite state transducers (FSTs) to take the sequence of dialog acts or strategies as inputs and output sequences of state embeddings for the hierarchical encoder-decoder (HED) part as inputs. The authors test their model on two tasks, a bargain task and a persuasion task.
The model that the authors prepose makes sense to me. It seems that the FSTs can successfully help the model to track the history better and propose useful state embeddings for the rest part of the model, on both dialog acts and strategies. Also, the authors have done a comprehensive empirical study to show the gain of the proposed techniques over the pure HED model and some baseline method. So I think this is a good work. But potentially it would be better if the authors can show more insights and details for the FSTs. 
Some detailed comments:
1. The example dialogs in Table 3 show us that the FSTs can output dialog act distributions that can help the agent behave better compared to the case with an RNN for tracking the history, and the case without FST/RNN. This shows the success of the FST framework.
2. The authors have done a comprehensive comparison for the cases with / without the two FSTs, as well as the case without the strategy predictor. Also, the authors compared with another existing method (Sequicity). The proposed method performs well compared to the baseline methods, on both general performances and the two types of human-evaluations. This shows the good empirical performance of the proposed method.
3. This paper can be better if the authors can provide more insights and details for the FSTs. For example, why FST is better than RNN? The authors mentioned something about that, but it is not clear enough and it will be great if the authors can provide more discussions on this. In addition, the output alphabets and the sets of states are not clear for the FSTs, it will be better if the authors show the full details for the FSTs, as in my opinion this is the major contribution of this work.
4. A minor comment: There are some inaccurate expressions in this paper. For example, the authors mentioned that the FSTs about put probabilistic density functions. However, to my understanding, the set of dialog acts is finite, hence FSTs output probabilistic mass functions, not probabilistic density functions. Please proofread about that.
Question:
1. Can you explain more about the choice for the parameters? For example, why the possible strategy output is a 15-dimensional binary-value vector?
2. For the loss L_{joint}, shouldn't it be (st_{t+1,j}\not\in u_{t+1}) in the indicator function?