Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation

Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan, Tanmoy Chakraborty, Md. Shad Akhtar


Abstract
The widespread online communication in a modern multilingual world has provided opportunities to blend more than one language (aka code-mixed language) in a single utterance. This has resulted a formidable challenge for the computational models due to the scarcity of annotated data and presence of noise. A potential solution to mitigate the data scarcity problem in low-resource setup is to leverage existing data in resource-rich language through translation. In this paper, we tackle the problem of code-mixed (Hinglish and Bengalish) to English machine translation. First, we synthetically develop HINMIX, a parallel corpus of Hinglish to English, with ~4.2M sentence pairs. Subsequently, we propose RCMT, a robust perturbation based joint-training model that learns to handle noise in the real-world code-mixed text by parameter sharing across clean and noisy words. Further, we show the adaptability of RCMT in a zero-shot setup for Bengalish to English translation. Our evaluation and comprehensive analyses qualitatively and quantitatively demonstrate the superiority of RCMT over state-of-the-art code-mixed and robust translation methods.
Anthology ID:
2024.lrec-main.1345
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
15480–15492
Language:
URL:
https://aclanthology.org/2024.lrec-main.1345
DOI:
Bibkey:
Cite (ACL):
Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan, Tanmoy Chakraborty, and Md. Shad Akhtar. 2024. Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15480–15492, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation (Kartik et al., LREC-COLING 2024)
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PDF:
https://aclanthology.org/2024.lrec-main.1345.pdf