NAACL 2021long29 citations
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.
BibTeX
@inproceedings{gupta-etal-2021-training,
title = "Training Data Augmentation for Code-Mixed Translation",
author = "Gupta, Abhirut and
Vavre, Aditya and
Sarawagi, Sunita",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.459/",
doi = "10.18653/v1/2021.naacl-main.459",
pages = "5760--5766"
}