Data Augmentation for Cross-Domain Named Entity Recognition

Shuguang Chen, Gustavo Aguilar, Leonardo Neves, Thamar Solorio


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 this work, we take this research direction to the opposite and 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.
Anthology ID:
2021.emnlp-main.434
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5346–5356
Language:
URL:
https://aclanthology.org/2021.emnlp-main.434
DOI:
10.18653/v1/2021.emnlp-main.434
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.434.pdf
Code
 ritual-uh/style_ner