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This paper discusses the approach used by the Accenture Team for CLEF2021CheckThat! Lab, Task 1, to identify whether a claim made in social media wouldbe interesting to a wide audience and should be fact-checked. Twitter trainingand test data were provided in English, Arabic, Spanish, Turkish, andBulgarian. Claims were to be classified (check-worthy/not check-worthy) andranked in priority order for the fact-checker. Our method used deep neuralnetwork transformer models with contextually sensitive lexical augmentationapplied on the supplied training datasets to create additional trainingsamples. This augmentation approach improved the performance for all languages.Overall, our architecture and data augmentation pipeline produced the bestsubmitted system for Arabic, and performance scales according to the quantityof provided training data for English, Spanish, Turkish, and Bulgarian. Thispaper investigates the deep neural network architectures for each language aswell as the provided data to examine why the approach worked so effectively forArabic, and discusses additional data augmentation measures that should couldbe useful to this problem.

Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation