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    <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.</abstract>
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%0 Conference Proceedings
%T Neural Cross-Lingual Named Entity Recognition with Minimal Resources
%A Xie, Jiateng
%A Yang, Zhilin
%A Neubig, Graham
%A Smith, Noah A.
%A Carbonell, Jaime
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xie-etal-2018-neural
%X 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.
%R 10.18653/v1/D18-1034
%U https://aclanthology.org/D18-1034
%U https://doi.org/10.18653/v1/D18-1034
%P 369-379

Markdown (Informal)

[Neural Cross-Lingual Named Entity Recognition with Minimal Resources](https://aclanthology.org/D18-1034) (Xie et al., EMNLP 2018)

• Neural Cross-Lingual Named Entity Recognition with Minimal Resources (Xie et al., EMNLP 2018)

ACL

• Jiateng Xie, Zhilin Yang, Graham Neubig, Noah A. Smith, and Jaime Carbonell. 2018. Neural Cross-Lingual Named Entity Recognition with Minimal Resources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 369–379, Brussels, Belgium. Association for Computational Linguistics.