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We leverage pre-trained language models to solve the task of complex NER fortwo low-resource languages: Chinese and Spanish. We use the technique of WholeWord Masking(WWM) to boost the performance of masked language modelingobjective on large and unsupervised corpora. We experiment with multiple neuralnetwork architectures, incorporating CRF, BiLSTMs, and Linear Classifiers ontop of a fine-tuned BERT layer. All our models outperform the baseline by asignificant margin and our best performing model obtains a competitive positionon the evaluation leaderboard for the blind test set.

Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages