@InProceedings{soto-hirschberg:2018:W18-32,
  author    = {Soto, Victor  and  Hirschberg, Julia},
  title     = {Joint Part-of-Speech and Language ID Tagging for Code-Switched Data},
  booktitle = {Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {1--10},
  abstract  = {Code-switching is the fluent alternation between two or more languages in conversation between bilinguals. Large populations of speakers code-switch during communication, but little effort has been made to develop tools for code-switching, including part-of-speech taggers. In this paper, we propose an approach to POS tagging of code-switched English-Spanish data based on recurrent neural networks. We test our model on known monolingual benchmarks to demonstrate that our neural POS tagging model is on par with state-of-the-art methods. We next test our code-switched methods on the Miami Bangor corpus of English Spanish conversation, focusing on two types of experiments: POS tagging alone, for which we achieve 96.34% accuracy, and joint part-of-speech and language ID tagging, which achieves similar POS tagging accuracy (96.39%) and very high language ID accuracy (98.78%). Finally, we show that our proposed models outperform other state-of-the-art code-switched taggers.},
  url       = {http://www.aclweb.org/anthology/W18-3201}
}