no code implementations • ACL (WebNLG, INLG) 2020 • Zdeněk Kasner, Ondřej Dušek
We describe our system for the RDF-to-text generation task of the WebNLG Challenge 2020.
1 code implementation • INLG (ACL) 2021 • Zdeněk Kasner, Simon Mille, Ondřej Dušek
Our system can detect the errors automatically using a combination of a rule-based natural language generation (NLG) system and pretrained language models (LMs).
1 code implementation • ACL 2022 • Zdeněk Kasner, Ondřej Dušek
In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise.
1 code implementation • INLG (ACL) 2020 • Ondřej Dušek, Zdeněk Kasner
A major challenge in evaluating data-to-text (D2T) generation is measuring the semantic accuracy of the generated text, i. e. checking if the output text contains all and only facts supported by the input data.
1 code implementation • INLG (ACL) 2020 • Zdeněk Kasner, Ondřej Dušek
Our approach maximizes the completeness and semantic accuracy of the output text while leveraging the abilities of recent pre-trained models for text editing (LaserTagger) and language modeling (GPT-2) to improve the text fluency.
no code implementations • 7 Apr 2020 • Zdeněk Kasner, Jindřich Libovický, Jindřich Helcl
Non-autoregressive (nAR) models for machine translation (MT) manifest superior decoding speed when compared to autoregressive (AR) models, at the expense of impaired fluency of their outputs.