Unsupervised Text Generation from Structured Data.
Martin M. Schmitt, Sahand Sharifzadeh, Volker Tresp, Hinrich Schütze · arXiv (Cornell University) · 2019
This work presents a joint solution to two challenging tasks: text generation from data and open information extraction. We propose to model both tasks as sequence-to-sequence translation problems and thus construct a joint neural model for both. Our experiments on knowledge graphs from Visual Genome, i.e., structured image analyses, shows promising results compared to strong baselines. Building on recent work on unsupervised machine translation, we report the first results - to the best of our knowledge - on fully unsupervised text generation from structured data.