تقرير
Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
| العنوان: | Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation |
|---|---|
| المؤلفون: | Kartik, Kartik, Soni, Sanjana, Kunchukuttan, Anoop, Chakraborty, Tanmoy, Akhtar, Md Shad |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 9 pages, 2 figures, to be published in LREC-COLING 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.16771 |
| رقم الأكسشن: | edsarx.2403.16771 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |