Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 8: Accept
review_content: The paper proposes an approach to generate textual descriptions from structured data organized in tables, by using a "variational template machine" (VTM), which is essentially a generative model to separately represent template and content as disentangled latent variables to control the generation.

The contribution is well-written and well-motivated, the model exposition is clear, and the results are convincing. The experiment setup, depth, and breadth are particularly convincing. I see no reason to not accept this paper.

Remarks:
- It should be clearly stated which languages feature in the paper. From what I gather, it's only English. How does the method generalize to other languages? How does it scale with (the lack of) resources?
review_title: Official Blind Review #2

final_score: rating: 3: Weak Reject
review_content: This paper proposes Variational Template Machine (VTM), a generative model to generate textual descriptions from structured data (i.e., tables). VTM is derived from the variational autoencoder, where the input is a row entry from a table and the output is the text associated with this entry. The authors introduce two latent variables to model contents and templates. The content variable is conditioned on the table entry, and generates the textual output together with the template variable. The model is trained on both paired table-to-text examples as well as unpaired (text only) examples. Experiments on the Wiki and SpNLG datasets show that models generate diverse sentences, and the overall performance in terms of BLEU is only slightly below the best baseline Table2Seq model that does not generate diverse sentences. The results also show that additional losses for preserving contents and templates introduced by the authors play an important role in the overall model performance. 

I have several questions regarding the experiments:
- For the Table2Seq baseline, how was the beam size chosen? Did it have any effect on the performance of the baseline model?
- Did the authors try other sampling methods for Table2Seq? (e.g., top-K or nucleus sampling)
- VTM is only able to achieve comparable performance to Table2Seq in terms of BLEU after including the unlabeled corpus, especially on the Wiki dataset. A way to incorporate this unlabeled data to Table2Seq is by first pretraining the LSTM generator on it before training it on pairwise data (or in parallel). How would this baseline model perform in comparison to VTM?
- In the conclusion section, the authors mentioned that VTM outperforms VAE both in terms of diversity and generation quality. What does this VAE model refer to? The experiments show that VTM is comparable to Table2Seq in terms of quality and is better in terms of diversity. 

Generating text from structured data is an interesting research area. However, I am not convinced that the proposed method is a significant development based on the results presented in the paper. There are also many grammatical errors in the paper (e.g., ... only enable to sample in the latent space ..., and many others), so I think the writing of the paper can be improved.
review_title: Official Blind Review #3

final_score: rating: 8: Accept
review_content: The paper is interesting and proposes a novel approach for addressing a currently not largely considered problem.
The proposed model is sound and appropriate, as it relies on state-of-the-art methodological arguments. 
The derivations are correct; this concerns both the model definition and the algorithmic derivations of model training and inference.
The experimental evaluation is adequate: it compares to many popular approaches and on several datasets; the outcomes are convincing.
It would be good if the authors could provide an analysis of the computational costs of their methods, as well as of the considered competitors. 
review_title: Official Blind Review #1

Meta-review: The paper addresses the problem of generating descriptions from structured data. In particular a Variational Template Machine  which explicitly disentangles templates from semantic content. They empirically demonstrate that their model performs better than existing methods on different methods. 

This paper has received a strong acceptance from two reviewers. In particular, the reviewers have appreciated the novelty and empirical evaluation of the proposed approach. R3 has raised quite a few concerns but I feel they were adequately addressed by the reviewers. Hence, I recommend that the paper be accepted. 
Decision: Accept (Poster)