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Many areas, such as the biological and healthcare domain, artistic works, andorganization names, have nested, overlapping, discontinuous entity mentionsthat may even be syntactically or semantically ambiguous in practice.Traditional sequence tagging algorithms are unable to recognize these complexmentions because they may violate the assumptions upon which sequence taggingschemes are founded. In this paper, we describe our contribution to SemEval2022 Task 11 on identifying such complex Named Entities. We have leveraged theensemble of multiple ELECTRA-based models that were exclusively pretrained onthe Bangla language with the performance of ELECTRA-based models pretrained onEnglish to achieve competitive performance on the Track-11. Besides providing asystem description, we will also present the outcomes of our experiments onarchitectural decisions, dataset augmentations, and post-competition findings.

TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla