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            <title>Proceedings of the 5th Workshop on Representation Learning for NLP</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Zero-Resource Cross-Domain Named Entity Recognition
%A Liu, Zihan
%A Winata, Genta Indra
%A Fung, Pascale
%Y Gella, Spandana
%Y Welbl, Johannes
%Y Rei, Marek
%Y Petroni, Fabio
%Y Lewis, Patrick
%Y Strubell, Emma
%Y Seo, Minjoon
%Y Hajishirzi, Hannaneh
%S Proceedings of the 5th Workshop on Representation Learning for NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-zero
%X 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.
%R 10.18653/v1/2020.repl4nlp-1.1
%U https://aclanthology.org/2020.repl4nlp-1.1
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.1
%P 1-6

Markdown (Informal)

[Zero-Resource Cross-Domain Named Entity Recognition](https://aclanthology.org/2020.repl4nlp-1.1) (Liu et al., RepL4NLP 2020)

• Zero-Resource Cross-Domain Named Entity Recognition (Liu et al., RepL4NLP 2020)

ACL

• Zihan Liu, Genta Indra Winata, and Pascale Fung. 2020. Zero-Resource Cross-Domain Named Entity Recognition. In Proceedings of the 5th Workshop on Representation Learning for NLP, pages 1–6, Online. Association for Computational Linguistics.