Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
This work addresses data scarcity for named entity recognition in specialized domains like materials science and biomedicine, but it is incremental as it applies an existing augmentation method to new data.
The authors tackled the problem of low-resource named entity recognition by adapting backtranslation to generate synthetic data, achieving improved performance on materials science and biomedical datasets, with concrete gains in low-resource scenarios.
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.