Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
<|reference_start|>Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation: 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.<|reference_end|>
@article{kartik2024synthetic,
    title={Synthetic Data Generation and Joint Learning for Robust Code-Mixed
  Translation},
    author={Kartik Kartik, Sanjana Soni, Anoop Kunchukuttan, Tanmoy Chakraborty,
  Md Shad Akhtar},
    journal={arXiv preprint arXiv:2403.16771},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16771},
    primaryClass={cs.CL cs.LG}
}