Publication at Faculty of Mathematics and Physics |
2020
Abstract
We describe our system for the RDF-to-text generation task of the WebNLG Challenge 2020. We base our approach on the mBART model, which is pre-trained for multilingual denoising.
This allows us to use a simple, identical, end-to-end setup for both English and Russian. Requiring minimal task or language-specific effort, our model placed in the first third of the leaderboard for English and first or second for Russian on automatic metrics, and it made it into the best or second-best system cluster on human evaluation.