We can build much better NLG systems if we understand what users want the systems to do! This may sound trite, but there is very little research in the academic community in understanding user needs and requirements, which is a shame and indeed lost opportunity.

Progress in NLG requires understanding what users want, creating high quality data sets, building models and algorithms, and thoroughly evaluating systems. I remain disappointed that the research community seems fixated on building models and pays much less attention to user needs, datasets, and evaluation.

Both academic researchers and commercial NLG developers are interested in building NLG systems which describe sporting events. However, they care about different things. For example, many academics show little interest in use cases, domain knowledge, robustness, and high quality input data, all of which are very important to commercial NLG developers.

NLG texts must be correct pragmatically as well as semantically. In particular, they must not contain statements which are contextually misleading even if they are literally true. We badly need better techniques for evaluating pragmatic accuracy as well as generating pragmatically correct texts.

There is a lot of uninformed criticism of rule-based NLG in academic papers. In this blog I explain at a very high level how such systems work and what some of the main challenges are in building them.

The fundamental challenges of building useful data-to-text NLG systems are the same regardless of whether we build systems with rules or transformers. We need to understand where NLG is useful, choose good content to communicate, robustly deal with edge cases, allow users to configure and control the system, and evaluate properly. I’d like to see more research on these fundamental issues, regardless of technology used.