@InProceedings{zou-EtAl:2018:C18-11,
  author    = {Zou, Bowei  and  Xu, Zengzhuang  and  Hong, Yu  and  Zhou, Guodong},
  title     = {Adversarial Feature Adaptation for Cross-lingual Relation Classification},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {437--448},
  abstract  = {Relation Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most existing methods focus on exploiting mono-lingual data, e.g., in English, due to the lack of annotated data in other languages. In this paper, we come up with a feature adaptation approach for cross-lingual relation classification, which employs a generative adversarial network (GAN) to transfer feature representations from one language with rich annotated data to another language},
  url       = {http://www.aclweb.org/anthology/C18-1037}
}