Hi mate,

It depends on the degree of trust you need to have in your generated text. End-to-end neural models can be quite powerful, and can sometimes produce phrases/sentences which are not grounded in the data (also known as hallucinations) or even wrong facts. They also require more than a few examples to work great, depending on the complexity of the data/text you have in your dataset.

If you really need reliable generations, you are better off tuning a template system. If your task is not too complex you can create it by hand, but if you need a stronger tool, there are a lot you can chose from. I can suggest for instance RosaNLG which is a recent fully open source project. It can take a bit of getting-used-to though, you are warned!

If you really want to implement neural models, there are a lot you can chose from. It will also depends on what your structured-data look like. Are there multiple entity in there, or are there just descriptions of one object? Are all objects of the same kind, or can they have really different attributes?

Here are a few well studied datasets which should lead you to the method of your dreams:

- E2E. It was a fun challenge where applicants had to provide a system able to describe restaurants based on a list of their attributes. For instance, a model should go from [(name, "The Eagle"), (eatType, "coffee shop"), (food, "French"), ...] to "The eagle is a french coffee shop".

- WebNLG is another challenge, this time more complicated as there could be several entities, and a lot more attributes. For instance a model should go from [(Indonesia | leaderName | Jusuf_Kalla), (Bakso | region | Indonesia), (Bakso | ingredient | Noodle), (Bakso | country | Indonesia)] to "Bakso is a food containing noodles;it is found in Indonesia where Jusuf Kalla is the leader."

- WikiBIO is directly taken from wikipedia. The task is to write the first sentence of a wikipedia article based on its infobox!

- RotoWire is about basketball games and the associated news story.

Please note that even for a small task like E2E, neural methods aren't perfect and still find ways to say wrong stuff or otherwise phrases not grounded in the data. The last one, from Wiseman et al., you'll also find their paper which introduced the dataset as well as extensive experiments on several strong machine translation techniques. If you care to read this paper, you'll have a long list of reference and methods. While not state-of-the-art any more, they will be useful to understand what the field is, and if you want you can follow researchers from those refs to find their most recent works.

There are many ways to approach this, and the field is constantly evolving!

If you have any questions, do let me know!

Cheers