Zero-Resource Cross-Domain Named Entity Recognition

Zihan Liu, Genta Indra Winata, Pascale Fung


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.
Anthology ID:
2020.repl4nlp-1.1
Volume:
Proceedings of the 5th Workshop on Representation Learning for NLP
Month:
July
Year:
2020
Address:
Online
Editors:
Spandana Gella, Johannes Welbl, Marek Rei, Fabio Petroni, Patrick Lewis, Emma Strubell, Minjoon Seo, Hannaneh Hajishirzi
Venue:
RepL4NLP
SIG:
SIGREP
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/2020.repl4nlp-1.1
DOI:
10.18653/v1/2020.repl4nlp-1.1
Bibkey:
Cite (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.
Cite (Informal):
Zero-Resource Cross-Domain Named Entity Recognition (Liu et al., RepL4NLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.repl4nlp-1.1.pdf
Video:
 http://slideslive.com/38929767
Data
CoNLL04