Zero-Resource Cross-Domain Named Entity Recognition

WS 2020  ·  Zihan Liu, Genta Indra Winata, Pascale Fung ·

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-the-art model which leverages extensive resources.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Cross-Domain Named Entity Recognition CoNLL04 BiLSTM w/ MTL and MoEE F1 70.04 # 1


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