Neural Cross-Lingual Named Entity Recognition with Minimal Resources

Abstract

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 low-resource language.

arXiv commentEMNLP 2018 long paper

References in corpus (10)

• Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition
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• Multilingual Models for Compositional Distributed Semantics
• Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields

Cited by in corpus (3)

• Cross-Lingual Transfer Learning for Multilingual Task Oriented Dialog
• Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations
• CalibreNet: Calibration Networks for Multilingual Sequence Labeling