Sakura at SemEval-2023 Task 2: Data Augmentation via Translation

Abstract

We demonstrate a simple yet effective approach to augmenting training data for multilingual named entity recognition using machine translation. The named entity spans from the original sentences are transferred to the translations via word alignment and then filtered with the baseline recognizer to retain high quality annotations. The proposed data augmentation approach improves the baseline performance of XLM-Roberta on the multilingual dataset.