  Automated fault diagnosis can facilitate diagnostics assistance, speedier
troubleshooting, and better-organised logistics. Currently, AI-based
prognostics and health management in the automotive industry ignore the textual
descriptions of the experienced problems or symptoms. With this study, however,
we show that a multilingual pre-trained Transformer can effectively classify
the textual claims from a large company with vehicle fleets, despite the task's
challenging nature due to the 38 languages and 1,357 classes involved. Overall,
we report an accuracy of more than 80% for high-frequency classes and above 60%
for above-low-frequency classes, bringing novel evidence that multilingual
classification can benefit automotive troubleshooting management.
