  We present our Generative Enhanced Model (GEM) that we used to create samples
awarded the first prize on the FEVER 2.0 Breakers Task. GEM is the extended
language model developed upon GPT-2 architecture. The addition of novel target
vocabulary input to the already existing context input enabled controlled text
generation. The training procedure resulted in creating a model that inherited
the knowledge of pretrained GPT-2, and therefore was ready to generate
natural-like English sentences in the task domain with some additional control.
As a result, GEM generated malicious claims that mixed facts from various
articles, so it became difficult to classify their truthfulness.
