From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text

Ishan Tarunesh, Syamantak Kumar, Preethi Jyothi


Abstract
Generating code-switched text is a problem of growing interest, especially given the scarcity of corpora containing large volumes of real code-switched text. In this work, we adapt a state-of-the-art neural machine translation model to generate Hindi-English code-switched sentences starting from monolingual Hindi sentences. We outline a carefully designed curriculum of pretraining steps, including the use of synthetic code-switched text, that enable the model to generate high-quality code-switched text. Using text generated from our model as data augmentation, we show significant reductions in perplexity on a language modeling task, compared to using text from other generative models of CS text. We also show improvements using our text for a downstream code-switched natural language inference task. Our generated text is further subjected to a rigorous evaluation using a human evaluation study and a range of objective metrics, where we show performance comparable (and sometimes even superior) to code-switched text obtained via crowd workers who are native Hindi speakers.
Anthology ID:
2021.acl-long.245
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3154–3169
Language:
URL:
https://aclanthology.org/2021.acl-long.245
DOI:
10.18653/v1/2021.acl-long.245
Bibkey:
Cite (ACL):
Ishan Tarunesh, Syamantak Kumar, and Preethi Jyothi. 2021. From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3154–3169, Online. Association for Computational Linguistics.
Cite (Informal):
From Machine Translation to Code-Switching: Generating High-Quality Code-Switched Text (Tarunesh et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.245.pdf
Video:
 https://aclanthology.org/2021.acl-long.245.mp4
Code
 ishan00/translation-for-code-switching-acl