Variational Template Machine for Data-to-Text Generation
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>, 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.
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.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Variational Template Machine for Data-to-Text Generation
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>, 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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



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.
assistant
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.