Abstract
Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities. CS texts have a complex interplay between languages and occur in informal contexts that make them harder to collect and construct NLP tools for. We approach this problem through Language Modeling (LM) on a new Hindi-English mixed corpus containing 59,189 unique sentences collected from blogging websites. We implement and discuss different Language Models derived from a multi-layered LSTM architecture. We hypothesize that encoding language information strengthens a language model by helping to learn code-switching points. We show that our highest performing model achieves a test perplexity of 19.52 on the CS corpus that we collected and processed. On this data we demonstrate that our performance is an improvement over AWD-LSTM LM (a recent state of the art on monolingual English).- Anthology ID:
- W18-3211
- 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:
- 92–97
- Language:
- URL:
- https://aclanthology.org/W18-3211
- DOI:
- 10.18653/v1/W18-3211
- Cite (ACL):
- Khyathi Chandu, Thomas Manzini, Sumeet Singh, and Alan W. Black. 2018. Language Informed Modeling of Code-Switched Text. In Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching, pages 92–97, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Language Informed Modeling of Code-Switched Text (Chandu et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/W18-3211.pdf
- Data
- WikiText-2