Neural Cross-Lingual Named Entity Recognition with Minimal Resources
Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, Jaime Carbonell · 2018
For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability.However, differences in words and word order across languages make it a challenging problem.To improve mapping of lexical items across languages, we propose a method that finds translations based on bilingual word embeddings.To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order.We demonstrate that these methods achieve state-of-the-art or competitive NER performance on commonly tested languages under a cross-lingual setting, with much lower resource requirements than past approaches.We also evaluate the challenges of applying these methods to Uyghur, a lowresource language.1