Synthetic Data Generation and Joint Learning for Robust Code-Mixed
  Translation
Kartik Sanjana Soni Anoop Kunchukuttan Tanmoy Chakraborty Md Shad Akhtar
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.