Cross-lingual intent classification in a low resource industrial setting

Abstract

This paper explores different approaches to multilingual intent classification in a low resource setting. Recent advances in multilingual text representations promise crosslingual transfer for classifiers. We investigate the potential for this transfer in an applied industrial setting and compare to multilingual classification using machine translated text. Our results show that while the recently developed methods show promise, practical application calls for a combination of techniques for useful results.