Name: Cross-lingual NER
Description: NER for five different target languages
  — Spanish, Dutch, German, Arabic and Finnish
Provider: Multiple institutions
Paper Title: Zero-Resource Cross-Lingual Named Entity Recognition
  
Tasks: named entity recognition
Venue Name: Association for the Advancement of Artificial Intelligence
Authors: M SAIFUL BARI,Shafiq R. Joty,Prathyusha Jwalapuram
Abstract: Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features. However, these models still require manually annotated training data, which is not available for many languages. In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary or parallel data. Our model achieves this through word-level adversarial learning and augmented fine-tuning with parameter sharing and feature augmentation. Experiments on five different languages demonstrate the effectiveness of our approach, outperforming existing models by a good margin and setting a new SOTA for each language pair.