  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 benefits of using a novel technique, originally proposed in the field of
image captioning, to account for potential negative effects of tuning models on
translated data. We show that translated data are of sufficient quality to tune
multilingual classifiers and that this novel loss technique is able to offer
some improvement over models tuned without it.
