  One of the most pressing societal issues is the fight against false news. The
false claims, as difficult as they are to expose, create a lot of damage. To
tackle the problem, fact verification becomes crucial and thus has been a topic
of interest among diverse research communities. Using only the textual form of
data we propose our solution to the problem and achieve competitive results
with other approaches. We present our solution based on two approaches - PLM
(pre-trained language model) based method and Prompt based method. The
PLM-based approach uses the traditional supervised learning, where the model is
trained to take 'x' as input and output prediction 'y' as P(y|x). Whereas,
Prompt-based learning reflects the idea to design input to fit the model such
that the original objective may be re-framed as a problem of (masked) language
modeling. We may further stimulate the rich knowledge provided by PLMs to
better serve downstream tasks by employing extra prompts to fine-tune PLMs. Our
experiments showed that the proposed method performs better than just
fine-tuning PLMs. We achieved an F1 score of 0.6946 on the FACTIFY dataset and
a 7th position on the competition leader-board.
