Zero-Resource Cross-Domain Named Entity Recognition
Zihan Liu, Genta Indra Winata, Pascale Fung · 2020
Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains.However, collecting data for low-resource target domains is not only expensive but also time-consuming.Hence, we propose a cross-domain NER model that does not use any external resources.We first introduce a Multi-Task Learning (MTL) by adding a new objective function to detect whether tokens are named entities or not.We then introduce a framework called Mixture of Entity Experts (MoEE) to improve the robustness for zero-resource domain adaptation.Finally, experimental results show that our model outperforms strong unsupervised cross-domain sequence labeling models, and the performance of our model is close to that of the state-of-theart model which leverages extensive resources.