This paper revisits the problem of sequence modeling using convolutional architectures. Although both convolutional and recurrent architectures have a long history in sequence prediction, the current "default" mindset in much of the deep learning community is that generic sequence modeling is best handled using recurrent networks. The goal of this paper is to question this assumption. Specifically, we consider a simple generic temporal convolution network (TCN), which adopts features from modern ConvNet architectures such as a dilations and residual connections. We show that on a variety of sequence modeling tasks, including many frequently used as benchmarks for evaluating recurrent networks, the TCN outperforms baseline RNN methods (LSTMs, GRUs, and vanilla RNNs) and sometimes even highly specialized approaches. We further show that the potential "infinite memory" advantage that RNNs have over TCNs is largely absent in practice: TCNs indeed exhibit longer effective history sizes than their recurrent counterparts. As a whole, we argue that it may be time to (re)consider ConvNets as the default "go to" architecture for sequence modeling.
The authors claim that convolutional networks should be considered as possible replacements of recurrent neural networks as the default choice for solving sequential modelling problems. The paper describes an architecture similar to wavenet with residual connections. Empirical results are presented on a large number of tasks where the convolutional network often outperforms modern recurrent baselines or reaches similar performance.
The biggest strength of the paper is the large number of tasks on which the models are evaluated. The experiments seem sound and the information in both the paper and the appendix seem to allow for replication. That said, I don’t think that all the tasks are very relevant for comparing convolutional and recurrent architectures. While the time windows that RNNs can deal with are infinite in principle, it is common knowledge that the effective length of the dependencies RNNs can model is quite limited in practise. Many of the artificial task like the adding problem and sequential MNIST have been designed to highlight this weakness of RNNs. I don’t find it very surprising that these tasks are easy to solve with a feedforward architecture with a large enough context window. The more impressive results are in my opinion those on the language modelling tasks where one would indeed expect RNNs to be more suitable for capturing dependencies that require stack-like memory functionality. 
While the related work is quite comprehensive, it downplays the popularity of convolutional architectures throughout history a bit. Especially in speech recognition, RNNs have only recently started to gain popularity while deep feedforward networks applied to overlapping time windows (i.e., 1D convolutions) have been the state-of-the-art for years. Of course the recent successes of dilated convolutions are likely to change the landscape in this application domain yet again.
The paper is well-structured and written. If anything, it is perhaps a little bit wordy at times but I prefer that over obscurity due to brevity.
The ideas in the paper are not novel and neither do the authors claim that they are. Unfortunately, I also think that the impact of the work is also somewhat limited due to the enormous success of the wavenet architecture. I do think that the results on the real-world tasks are valuable and worthy of publication. However, I feel that the authors exaggerate the extent to which researchers in this field still consider RNNs superior models for sequences. 
+ Many experiments and tasks.
+ Well-written and clear.
+ Good results
- Somewhat exaggerated claims about the extent to which RNNs are still being considered more suitable sequence models
 than dilated convolutions. Especially in light of the success of Wavenet.
- Not much novelty/originality.