Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation

• URL: http://arxiv.org/abs/2108.11703v1
• Date: Thu, 26 Aug 2021 10:56:39 GMT
• Title: Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
• Authors: Usama Yaseen, Stefan Langer
• Abstract summary: We adapt backtranslation to generate high quality and linguistically diverse synthetic data for low-resource named entity recognition. We perform experiments on two datasets from the materials science (MaSciP) and biomedical domains (S800)
• Score: 1.195496689595016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The state of art natural language processing systems relies on sizable training datasets to achieve high performance. Lack of such datasets in the specialized low resource domains lead to suboptimal performance. In this work, we adapt backtranslation to generate high quality and linguistically diverse synthetic data for low-resource named entity recognition. We perform experiments on two datasets from the materials science (MaSciP) and biomedical domains (S800). The empirical results demonstrate the effectiveness of our proposed augmentation strategy, particularly in the low-resource scenario.

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