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This paper addresses the question as to what degree a BERT-based multilingualSpoken Language Understanding (SLU) model can transfer knowledge acrosslanguages. Through experiments we will show that, although it workssubstantially well even on distant language groups, there is still a gap to theideal multilingual performance. In addition, we propose a novel BERT-basedadversarial model architecture to learn language-shared and language-specificrepresentations for multilingual SLU. Our experimental results prove that theproposed model is capable of narrowing the gap to the ideal multilingualperformance.

To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding?