  This paper discusses the approach used by the Accenture Team for CLEF2021
CheckThat! Lab, Task 1, to identify whether a claim made in social media would
be interesting to a wide audience and should be fact-checked. Twitter training
and test data were provided in English, Arabic, Spanish, Turkish, and
Bulgarian. Claims were to be classified (check-worthy/not check-worthy) and
ranked in priority order for the fact-checker. Our method used deep neural
network transformer models with contextually sensitive lexical augmentation
applied on the supplied training datasets to create additional training
samples. This augmentation approach improved the performance for all languages.
Overall, our architecture and data augmentation pipeline produced the best
submitted system for Arabic, and performance scales according to the quantity
of provided training data for English, Spanish, Turkish, and Bulgarian. This
paper investigates the deep neural network architectures for each language as
well as the provided data to examine why the approach worked so effectively for
Arabic, and discusses additional data augmentation measures that should could
be useful to this problem.
