Training Data Augmentation for Code-Mixed Translation

Abhirut Gupta, Aditya Vavre, Sunita Sarawagi


Abstract
Machine translation of user-generated code-mixed inputs to English is of crucial importance in applications like web search and targeted advertising. We address the scarcity of parallel training data for training such models by designing a strategy of converting existing non-code-mixed parallel data sources to code-mixed parallel data. We present an m-BERT based procedure whose core learnable component is a ternary sequence labeling model, that can be trained with a limited code-mixed corpus alone. We show a 5.8 point increase in BLEU on heavily code-mixed sentences by training a translation model using our data augmentation strategy on an Hindi-English code-mixed translation task.
Anthology ID:
2021.naacl-main.459
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5760–5766
Language:
URL:
https://aclanthology.org/2021.naacl-main.459
DOI:
10.18653/v1/2021.naacl-main.459
Bibkey:
Cite (ACL):
Abhirut Gupta, Aditya Vavre, and Sunita Sarawagi. 2021. Training Data Augmentation for Code-Mixed Translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5760–5766, Online. Association for Computational Linguistics.
Cite (Informal):
Training Data Augmentation for Code-Mixed Translation (Gupta et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.459.pdf
Video:
 https://aclanthology.org/2021.naacl-main.459.mp4
Code
 shruikan20/spoken-tutorial-dataset