How to generate descriptions from structured data organized in tables? Existing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an open set of templates is crucial for enriching the phrase constructions and realizing varied generations. Learning such templates is prohibitive since it often requires a large paired <table,description> corpus, which is seldom available. This paper explores the problem of automatically learning reusable "templates" from paired and non-paired data. We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables. Our contributions include: a) we carefully devise a specific model architecture and losses to explicitly disentangle text template and semantic content information, in the latent spaces, and b) we utilize both small parallel data and large raw text without aligned tables to enrich the template learning. Experiments on datasets from a variety of different domains show that VTM is able generate more diversely while keeping a good fluency and quality.
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.