Improving Neural Machine Translation Through Code-Mixed Data Augmentation

Abstract

This paper studies neural machine translation (NMT) of code-mixed (CM) text. Specifically, we generate synthetic CM data and how it can be used to improve the translation performance of NMT through the data augmentation strategy. We conduct experiments on three data augmentation approaches viz. CM-Augmentation, CM-Concatenation, and Multi-Encoder approaches, and the latter two approaches are inspired by document-level NMT, where we use synthetic CM data as context to improve the performance of the NMT models. We conduct experiments on three language pairs, viz. Hindi–English, Telugu–English and Czech–English. Experimental results demonstrate that the proposed approaches significantly improve performance over the baseline model trained without data augmentation and over the existing data augmentation strategies. The CM-Concatenation model attains the best performance.