  In this paper, we report improved results of the Fake News Challenge Stage 1
(FNC-1) stance detection task. This gain in performance is due to the
generalization power of large language models based on Transformer
architecture, invented, trained and publicly released over the last two years.
Specifically (1) we improved the FNC-1 best performing model adding BERT
sentence embedding of input sequences as a model feature, (2) we fine-tuned
BERT, XLNet, and RoBERTa transformers on FNC-1 extended dataset and obtained
state-of-the-art results on FNC-1 task.
