Paper:
We propose a neural clustering model that jointly learns both latent features and how they cluster. Unlike similar methods our model does not require a predefined number of clusters. Using a supervised approach, we agglomerate latent features towards randomly sampled targets within the same space whilst progressively removing the targets until we are left with only targets which represent cluster centroids. To show the behavior of our model across different modalities we apply our model on both text and image data and achieve very competitive results on MNIST against methods that require a predefined number of clusters. We also provide results against baseline models for fashion-MNIST, the 20 newsgroups dataset, and a Twitter dataset we ourselves create.
1 .

Review:
This paper proposes a neural clustering model following the "Noise as Target" technique. Combining with an reconstruction objective and "delete-and-copy" trick, it is able to cluster the data points into different groups and is shown to give competitive results on different benchmarks.
It is nice that the authors tried to extend the "noise as target" to the clustering problem, and proposed the simple "delete-and-copy" technique to group different data points into clusters. Even tough a little bit ad-hoc, it seems promising based on the experiment results. However, it is unclear to me why it is necessary to have the optimal matching here and why the simple nearest target would not work. After all, the cluster membership is found based on the nearest target in the test stage. 
Also, the authors should provide more detailed description regarding the scheduling of the alpha and lambda values during training, and how sensitive it is to the final clustering performance. The authors cited the no requirement of "a predefined number of clusters" as one of the contributions, but the tuning of alpha seems more concerning.
I like the authors experimented with different benchmarks, but lack of comparisons with existing deep clustering techniques is definitely a weakness. The only baseline comparison provided is the k-means clustering, but the comparisons were somewhat unfair. For all the text datasets, there were no comparisons with k-means on the features learned from the auto-encoders or clusterings learned from similar number of clusters. The comparisons for the Twitter dataset were even based on character-level with word-level. It is more convincing to show the superiority of the proposed method than existing ones on the same ground.
Some other issues regarding quantitative results:
- In Table 1, there are 152 clusters for 10-d latent space after convergence, but there are 61 clusters for 10-d latent space in Table 2 for the same MNIST dataset. Are they based on different alpha and lambda values? 
- Why does NATAC perform much better than NATAC-k? Would NATAC-k need a different number of clusters than the one from NATAC? The number of centroids learned from NATAC may not be good for k-means clustering.
- It seems like the performance of AE-k is increasing with increase of dimensionality of latent space for Fashion-MNIST. Would AE-k beat NATAC with a different dimensionality of latent space and k?