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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.repl4nlp-1.1.pdf