Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translation
It addresses data scarcity and noise in low-resource code-mixed translation, with incremental improvements for multilingual NLP applications.
The paper tackles code-mixed (Hinglish and Bengalish) to English machine translation by generating a synthetic parallel corpus of ~4.2M sentence pairs and proposing a joint-training model that handles noise, showing superiority over state-of-the-art methods in evaluations.
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.