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This paper summarizes the CLaC submission for the MultiCoNER 2 task whichconcerns the recognition of complex, fine-grained named entities. We comparetwo popular approaches for NER, namely Sequence Labeling and Span Prediction.We find that our best Span Prediction system performs slightly better than ourbest Sequence Labeling system on test data. Moreover, we find that using thelarger version of XLM RoBERTa significantly improves performance.Post-competition experiments show that Span Prediction and Sequence Labelingapproaches improve when they use special input tokens (<s> and </s>) ofXLM-RoBERTa. The code for training all models, preprocessing, andpost-processing is available athttps://github.com/harshshredding/semeval2023-multiconer-paper.

CLaC at SemEval-2023 Task 2: Comparing Span-Prediction and Sequence-Labeling approaches for NER