论文标题
在基于BERT的多语言口语理解中，语言边界会在什么程度上模糊？
To What Degree Can Language Borders Be Blurred In BERT-based Multilingual Spoken Language Understanding?
论文作者
论文摘要
本文解决了一个问题，即基于BERT的多语言语言理解（SLU）模型可以在多大程度上转移知识。通过实验，我们将证明，尽管它在遥远的语言群体上也可以很好地工作，但理想的多语言表现仍然存在差距。此外，我们提出了一种基于BERT的新型对抗模型体系结构，以学习多语言SLU的语言共享和特定语言的表示。我们的实验结果证明，所提出的模型能够缩小与理想多语言性能的差距。
This paper addresses the question as to what degree a BERT-based multilingual Spoken Language Understanding (SLU) model can transfer knowledge across languages. Through experiments we will show that, although it works substantially well even on distant language groups, there is still a gap to the ideal multilingual performance. In addition, we propose a novel BERT-based adversarial model architecture to learn language-shared and language-specific representations for multilingual SLU. Our experimental results prove that the proposed model is capable of narrowing the gap to the ideal multilingual performance.
