|Claire Gardent CNRS/LORIA|
Natural Language Generation (NLG) aims at creating text based on some input (data, text, meaning representation) and some communicative goal (summarising, verbalising, comparing etc.). In the pre-neural era, differing input types and communicative goals led to distinct computational models. In contrast, deep learning encoder-decoder models introduced a shift of paradigm in that they provide a unifying framework for all NLG tasks. In my talk, I will start by briefly introducing the three main types of input considered in NLG. I will then give an overview of how neural models handle these and present some of the work we did on generating text from meaning representations, from data and from text.