Using Machine Translation to Augment Multilingual Classification

arXiv:2405.05478v1 Announce Type: new
Abstract: An all-too-present bottleneck for text classification model development is the need to annotate training data and this need is multiplied for multilingual classifiers. Fortunately, contemporary machine translation models are both easily accessible and have dependable translation quality, making it possible to translate labeled training data from one language into another. Here, we explore the effects of using machine translation to fine-tune a multilingual model for a classification task across multiple languages. We also investigate the …

abstract arxiv augment classification classification model classifiers cs.cl data development language machine machine translation making model development multilingual quality text text classification training training data translate translation type