Efficient Data Augmentation via lexical matching for boosting performance on Statistical Machine Translation for Indic and a Low-resource language

With the fast advancement of AI technology in recent years, many excellent Data Augmentation (DA) approaches have been investigated to increase data efficiency in Natural Language Processing (NLP). The reliance on a large amount of data prohibits NLP models from performing tasks such as labelling enormous amounts of textual data, which require a substantial amount of time, money, and human resources; hence, a better model requires more data. Text DA technique rectifies the data by extending it, enhancing the model's accuracy and resilience. A novel lexical-based matching approach is the cornerstone of this work; it is used to improve the quality of the Machine Translation (MT) system. This study includes resource-rich Indic (i.e., Indo-Aryan and Dravidian language families) to examine the proposed techniques. Extensive experiments on a range of language pairs depict that the proposed method significantly improves scores in the enhanced dataset compared to the baseline system's BLEU, METEOR and ROUGE evaluation scores.