@inproceedings{liu-etal-2020-zero,
title = "Zero-Resource Cross-Domain Named Entity Recognition",
author = "Liu, Zihan and
Winata, Genta Indra and
Fung, Pascale",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.1",
doi = "10.18653/v1/2020.repl4nlp-1.1",
pages = "1--6",
abstract = "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.",
}
Markdown (Informal)
[Zero-Resource Cross-Domain Named Entity Recognition](https://aclanthology.org/2020.repl4nlp-1.1) (Liu et al., RepL4NLP 2020)
ACL