CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing

Sai Muralidhar Jayanthi, Kavya Nerella, Khyathi Raghavi Chandu, Alan W Black


Abstract
The NLP community has witnessed steep progress in a variety of tasks across the realms of monolingual and multilingual language processing recently. These successes, in conjunction with the proliferating mixed language interactions on social media, have boosted interest in modeling code-mixed texts. In this work, we present CodemixedNLP, an open-source library with the goals of bringing together the advances in code-mixed NLP and opening it up to a wider machine learning community. The library consists of tools to develop and benchmark versatile model architectures that are tailored for mixed texts, methods to expand training sets, techniques to quantify mixing styles, and fine-tuned state-of-the-art models for 7 tasks in Hinglish. We believe this work has the potential to foster a distributed yet collaborative and sustainable ecosystem in an otherwise dispersed space of code-mixing research. The toolkit is designed to be simple, easily extensible, and resourceful to both researchers as well as practitioners. Demo: <http://k-ikkees.pc.cs.cmu.edu:5000> and Library: <https://github.com/murali1996/CodemixedNLP>
Anthology ID:
2021.calcs-1.14
Volume:
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Month:
June
Year:
2021
Address:
Online
Venues:
CALCS | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
113–118
Language:
URL:
https://aclanthology.org/2021.calcs-1.14
DOI:
10.18653/v1/2021.calcs-1.14
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.calcs-1.14.pdf
Code
 murali1996/CodemixedNLP