Abstract
Language identification (LID) is a critical first step for processing multilingual text. Yet most LID systems are not designed to handle the linguistic diversity of global platforms like Twitter, where local dialects and rampant code-switching lead language classifiers to systematically miss minority dialect speakers and multilingual speakers. We propose a new dataset and a character-based sequence-to-sequence model for LID designed to support dialectal and multilingual language varieties. Our model achieves state-of-the-art performance on multiple LID benchmarks. Furthermore, in a case study using Twitter for health tracking, our method substantially increases the availability of texts written by underrepresented populations, enabling the development of “socially inclusive” NLP tools.- Anthology ID:
- P17-2009
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 51–57
- Language:
- URL:
- https://aclanthology.org/P17-2009
- DOI:
- 10.18653/v1/P17-2009
- Cite (ACL):
- David Jurgens, Yulia Tsvetkov, and Dan Jurafsky. 2017. Incorporating Dialectal Variability for Socially Equitable Language Identification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 51–57, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Incorporating Dialectal Variability for Socially Equitable Language Identification (Jurgens et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/P17-2009.pdf