"Local Additivity Based Data Augmentation for Semi-supervised NER" is work conducted by Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang and Diyi Yang at the Machine Learning Center at Georgia Tech, ASIT Japan/Google, and Citadel Securities.
This paper was accepted to the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the dependence on labeled data, we propose a Local Additivity based Data Augmentation (LADA) method for semi-supervised NER, in which we create virtual samples by interpolating sequences close to each other. Our approach has two variations: Intra-LADA and InterLADA, where Intra-LADA performs interpolations among tokens within one sentence, and Inter-LADA samples different sentences to interpolate. Through linear additions between sampled training data, LADA creates an infinite amount of labeled data and improves both entity and context learning. We further extend LADA to the semi-supervised setting by designing a novel consistency loss for unlabeled data. Experiments conducted on two NER benchmarks demonstrate the effectiveness of our methods over several strong baselines. We have publicly released our code at https://github.com/GT-SALT/LADA.
Full paper: https://arxiv.org/pdf/2010.01677.pdf
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