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.- Anthology ID:
- W18-3201
- Volume:
- Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–10
- Language:
- URL:
- https://aclanthology.org/W18-3201
- DOI:
- 10.18653/v1/W18-3201
- Cite (ACL):
- Victor Soto and Julia Hirschberg. 2018. Joint Part-of-Speech and Language ID Tagging for Code-Switched Data. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 1–10, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Joint Part-of-Speech and Language ID Tagging for Code-Switched Data (Soto & Hirschberg, ACL 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-3201.pdf
- Data
- Penn Treebank