Data Augmentation for Cross-Domain Named Entity Recognition

• URL: http://arxiv.org/abs/2109.01758v1
• Date: Sat, 4 Sep 2021 00:50:55 GMT
• Title: Data Augmentation for Cross-Domain Named Entity Recognition
• Authors: Shuguang Chen, Gustavo Aguilar, Leonardo Neves and Thamar Solorio
• Abstract summary: We study cross-domain data augmentation for the named entity recognition task. We propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain. We show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.
• Score: 22.66649873447105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limited. In contrast, we study cross-domain data augmentation for the NER task. We investigate the possibility of leveraging data from high-resource domains by projecting it into the low-resource domains. Specifically, we propose a novel neural architecture to transform the data representation from a high-resource to a low-resource domain by learning the patterns (e.g. style, noise, abbreviations, etc.) in the text that differentiate them and a shared feature space where both domains are aligned. We experiment with diverse datasets and show that transforming the data to the low-resource domain representation achieves significant improvements over only using data from high-resource domains.

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