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

arXiv:2403.16771v1 Announce Type: cross
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 …

abstract annotated data arxiv blend challenge code communication computational cs.cl cs.lg data language mixed modern multilingual opportunities robust synthetic synthetic data translation type world