Joint Part-of-Speech and Language ID Tagging for Code-Switched Data

Victor Soto, Julia Hirschberg


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W18-3201.pdf
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